Find Your Dream Artificial Intelligence Job in India

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance's AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workflow Orchestration

• Build multi-step AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Implement agent-style workflows where models interact with APIs, tools, and internal systems.
• Ensure workflows are observable, reliable, and maintainable in production environments.

Evaluation & Reliability

• Define evaluation approaches for measuring response quality, hallucination rates, and accuracy.
• Monitor latency, token usage, and cost efficiency of model interactions.
• Continuously improve system performance through experimentation and benchmarking.

Competencies & Professional Background

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.

• Experience
• 8+ years of software engineering or AI engineering experience.
• Of which 2+ years of hands-on experience working with LLMs, Generative AI, or advanced NLP-based AI systems.
• Experience deploying AI-powered applications into production environments.
• Technical Skills
• Strong programming skills in Python.
• Experience with LLM frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of prompt design, structured prompting, and prompt iteration strategies.
• Experience designing RAG pipelines and working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Familiarity with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
• Experience integrating AI services with REST APIs and microservices.
• Understanding of evaluation, monitoring, and guardrails for LLM systems.
• Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business requirements into practical AI solutions.
• Comfortable collaborating with product, architecture, and engineering teams.
• Curiosity and willingness to stay current with evolving AI tools and frameworks.

Thanks & Regards,

Sohail Khan

Talent Acquisition Teleperformance

E-mail:

AI Development Engineer

Experience : 3 to 5 Years

Location : Hyderabad

Notice Period: 30 Days

Job Description:

Robotics Development Engineer to build hardware-accelerated, AI-enabled robotics

software stack for autonomous mobile robots (AMRs) and intelligent robotic systems. In

this role, you will design and implement robotics software that leverages heterogeneous

compute (CPU + GPU + NPU ) to deliver high-performance perception, localization,

planning, and control. You will work across ROS 2, AI pipelines, simulation, and

hardware acceleration, helping bring next-generation autonomous systems from concept

to production.

Preferred Experience:

• Engineer with 8 to 15yrs of experience in embedded Linux/Linux application development.
• More than 5 year of Robotics Development experience
• Expertise in ROS 2
• Understanding of robotics fundamentals: SLAM, navigation, motion planning, control
• Hands-on experience with robotics simulation tools (Gazebo/Ignition, Isaac Sim)
• Strong C/C++ development skills with a good understanding of object-oriented design.
• Experience dealing with Linux community and Open Source contribution a plus
• Experience with ROS2, Robotics Sensors, S

About the Company

Bonfiglioli Technology Space is a GCC of Bonfiglioli Italy. We are enhancing out AI capability in India to cater to global needs Bonfiglioli is a gear box manufacturing company Head quartered at Italy for 70 years and is 25 years in India. With GCC office located in Pallavaram, Chennai, We have 3 manufacturing plants in India with over 800+ employees

About the Role

We are looking for an AI professional with strong expertise in building and deploying end-to-end Machine Learning (ML) and Natural Language Processing (NLP) solutions, including Large Language Models (LLMs) and advanced AI workflow

The ideal candidate should have hands-on experience in AI solution design, implementation, and deployment, with a focus on LLM-based applications, RAG pipelines, embeddings, and vector retrieval.

Responsibilities

• Building and deploying end-to-end Machine Learning (ML) and Natural Language Processing (NLP) solutions.
• Focusing on LLM-based applications, RAG pipelines, embeddings, and vector retrieval.
• Demonstrating excellent problem-solving skills.
• Working in cross-functional teams.
• Taking a proactive approach to innovation.

Qualifications & Years of experience

• Hands-on experience in AI solution design, implementation, and deployment.
• 5-8 years of experience in AI solutions i

The AI Engineer is a hands-on contributor responsible for building, extending, and delivering AI solutions at proMX. This role focuses on implementing Copilot Studio Agents (low‑code + pro‑code), integrating AI capabilities across Microsoft platforms, and supporting both R&D initiatives and customer projects.

The AI Engineer works under the Technical Lead AI, contributes to reusable components and best practices, and plays a key role in enabling junior team members and project teams.

Region: Global – must be available for collaboration in IST and CEST/CET time zones.

Key Responsibilities:

AI Solution Development

• Build, configure, and maintain Microsoft Copilot Studio Agents (low‑code + pro‑code extensions).
• Implement AI features across Power Platform, Dynamics 365, and Dataverse.
• Develop basic pro‑code components (C#, JavaScript/TypeScript, or Python) when required.
• Support experimentation with Microsoft Foundry, AI extensibility options, and new AI capabilities.

Hands‑On Implementation

• Translate functional requirements into working AI components.
• Debug issues, optimize agent behaviors, integrate APIs, and validate data flows.
• Maintain internal templates, reusable components, and coding guidelines.

Team Support & Mentoring

• Act as a first technical point of contact

Hi All,

We are hiring for Senior AI Engineer role.

Candidate from Tier 1 Colleges will be considered for this role.

Work Mode: Remote

Shift: ( US Shift and night shift)

Teleperformance’s AIML/GenAI ecosystem is a fast-paced and dynamic environment. As the Senior AI Engineer

KEY RESPONSIBILITIES & EXPECTATIONS

LLM Application Development

• Build and deploy LLM-powered applications for use cases such as automation, knowledge retrieval, and conversational workflows.
• Design prompt structures and reasoning flows that produce consistent and reliable outputs.
• Integrate LLMs with internal services, APIs, and enterprise data sources.

Prompt Engineering

• Develop prompt templates, few-shot examples, and structured output formats.
• Improve reliability by implementing validation layers, guardrails, and fallback strategies.
• Continuously iterate on prompts based on real usage patterns and feedback.

Retrieval & Knowledge Integration

• Design retrieval pipelines using vector databases and semantic search.
• Integrate enterprise knowledge bases and structured data into LLM workflows.
• Improve response grounding by optimizing document chunking, embeddings, and context management.

AI Workf

Job Description: Sr. Staff Software Engineer – Artificial Intelligence

Experience Level : 4-6 Years

Position Overview

We are seeking a highly motivated, visionary and technically accomplished AI Engineer/Software developer to joint our team and lead the design, development, and deployment of advanced AI and machine learning solutions. This role will focus on building agentic AI systems, generative AI workflows, and scalable data platforms that power intelligent decision-making across the enterprise. The ideal candidate will possess deep expertise in AI/ML, software engineering, and cloud-native architecture, with a passion for solving complex problems using cutting-edge technologies.

Key Responsibilities

• Develop Algorithms that enable AI agents to perform tasks without step-by-step instructions.
• Design and implement agentic AI systems capable of autonomous decision-making, learning from experience, and adapting to dynamic goals and contexts
• Design and develop multi-agent frameworks using tools such as LangGraph, Crew AI, or Semantic Kernel to orchestrate intelligent workflows.
• Design and develop machine learning techniques that allow agents to learn from experience and adapt over time.
• Translate business requirements into agentic AI solutions.
• Collaborate with data scientists, software engineers, business stakeholders, and p

We are hiring for Agentic AI Lead for Bangalore location.

Experience required- 8 to 13 years

Please share CV to monica_sharma@hcl-software.com with the below details:

Total Experience-

Current CTC-

Expected CTC-

Notice Period-

Mandatory skills:

1. Member of a multidisciplinary team able to experiment in a rapid mode - to define, build and validate solutions in a agile fashion
2. Programming: Python, Java, Javascript (any 2)
3. Frameworks/Protocols: AgenticAI frameworks - Open Source - langchain, crew.ai & commercial offerings (CoPilot, AgentSpace etc.), MCP, A2A etc.
4. ML Architectures: GenAI (must have), any 2 of following (Deep Learning, NLP, TensorFlow, PyTorch, and scikit-learn)
5. Software Architecture: Architectural patterns and their application, tradition and AI applications
6. Platforms: AWS, Azure, or Google Cloud, ServiceNow, SalesForce (hands on knowledge with any 2-3)

Job Description:

• Product Development with 8+ years of experience

• BE/BTech (or above) in Computer Science or related streams from premier institutes only

• Extensive experience in AI/ML technologies and software development in premiere setups

• Excellent consulting skills

• Expertise in Pyt

About the Company

Our client is a globally recognized, innovation-led technology firm specializing in data, artificial intelligence, and modern digital engineering. With a strong presence across the United States and India, the organization collaborates with leading enterprises to design and deliver intelligent, scalable solutions that enhance decision-making and drive business transformation.

Renowned for tackling complex, high-value challenges, the company blends deep technical expertise with forward-thinking approaches across Data Engineering, AI, and next-generation software development.

Role Summary

We are seeking a dynamic and visionary leader to head the Data Science function. This role will be instrumental in shaping the organization’s AI strategy, expanding its data science capabilities, and delivering impactful, enterprise-grade analytics and machine learning solutions.

The ideal candidate brings a balance of strategic thinking, hands-on expertise, and leadership experience, with a strong ability to connect data science initiatives to tangible business outcomes.

Key Responsibilities

Strategic Leadership

• Define and drive the long-term vision for Data Science and AI aligned with organizational goals
• Build and scale a high-performing, globally distributed data science team
• Establish robust frameworks, operating models, and best practices ac

6-month contract, which will convert into a full-time permanent position afterward.

AI/ML Engineer (LLMs & Generative AI)

Location: Bengaluru or Pune (Hybrid: 3 days onsite, 2 days remote)

ONLY APPLY IF YOU CAN JOIN ASAP OR IF YOU HAVE ALREADY RESIGNED AND CAN JOIN IN 15 DAYS!

We are looking for a skilled AI/ML Engineer with hands-on experience in AI/ML development and Large Language Models (LLMs).

Key Skills:

• 5-10 years in Python, PyTorch, TensorFlow, PySpark, Scikit-Learn, SQL
• Expertise in LLMs & GenAI: LangChain, Hugging Face, OpenAI GPT, Gemini, LLaMA
• NLP, data visualization (Tableau/Power BI)
• MLOps/LLMOps: MLflow, Docker, Kubernetes, CI/CD, FastAPI, Streamlit
• Cloud AI: AWS (SageMaker, S3), Azure AI/OpenAI, GCP Vertex AI

Responsibilities:

• Develop, deploy, and optimize AI/ML & LLM solutions
• Implement NLP applications and AI pipelines
• Collaborate on scalable AI solutions and production-grade deployments

Company Description

HEAPS is one of the fastest growing health tech start-ups in India today. With more than 5 million patient interactions till date, Heaps is revolutionising how insurers, doctors and patients experience health care. We are building an AI driven end-to-end care management platform which coordinates, tracks and monitors the health of the patient empowering them with the right information to take right decisions at the right time about their health.

Heaps is backed by marquee investors - titans in the healthcare industry who have invested more than $7.4 million in Series A funding. Heaps is on a path for global scaling, we’ve already expanded in 5 countries across the globe.

Role Description

Are you an AI engineer with a strong technical background and a passion for solving complex real-world problems? At HEAPS, our AI team drives innovation by developing and integrating advanced machine learning and Large Language Models (LLMs) into healthcare products. We are seeking skilled AI Engineers to lead end-to-end AI initiatives—designing, fine-tuning, and deploying models that extract actionable insights from healthcare data. Your work will directly support insurance and hospital clients worldwide in improving patient care and operational outcomes through scalable AI solutions.

Responsibilities

· Design, develop, and integrate Large Language Models (LLMs) into our healthca

Healthcare communication is broken. At PypeAI , we are building the "Operating Layer" for healthcare voice first agents that manage the entire patient journey, from clinical triage to automated follow-ups. We are building the infrastructure for autonomous healthcare coordination.

As an AI Engineer, you won't be on the sidelines; you’ll be at the core of our engine. You will work directly on our Voice-to-Voice (V2V) pipeline, pushing the boundaries of what’s possible in real-time, empathetic, and clinically reliable AI.

• Fine-tuning Models: Optimize domain-specific LLMs and Audio Models for medical accuracy and human-like prosody.
• V2V Pipeline: Engineer the cutting edge of STT (Speech-to-Text) and TTS (Text-to-Speech) to minimize latency to sub-second levels.
• Inference at Scale: Deploy and optimize models to handle thousands of concurrent, high-stakes medical conversations.
• Edge-of-the-Map Engineering: Work on the entire pipeline—from data synthesis to production-grade inference.

Qualification

We are looking for someone who doesn't just "use" AI but understands how it breathes.

• Experience: 1-5 years in AI
• You have hands-on experience with PyTorch/TensorFlow
• have fine-tuned models or trained from scratch
• You understand audio processing (sample rates,

AI Engineer – ML Fundamentals, LLM/SLM, Generative AI | Fintech Product

Location: Bengaluru (In-office)

Experience: 3-5 years

About the Role

We’re hiring an AI Engineer to build next-gen AI systems powering a high-growth fintech product used by millions. You’ll work on LLMs, GenAI, personalization, and real-time intelligence embedded directly into customer workflows.

What You’ll Do

• Strong exposure towards Python programming language
• Build & fine-tune LLM/Transformer-based models for real product features
• Design RAG pipelines, AI agents, and recommendation systems
• Apply classical ML + deep learning to financial and behavioral data
• Deploy and optimize models in cloud production environments
• Collaborate closely with product & engineering teams

Must-Have Skills

• Strong Python + PyTorch/TensorFlow
• Hands-on LLMs / GenAI / Transformers
• Experience with RAG, embeddings, prompt engineering
• Production ML experience on AWS/GCP/Azure

Why Join

• Work on AI-first fintech products with real user impact
• High ownership, fast growth, ESOPs
• Solve hard problems at scale with a strong engineering team


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