Find Your Dream Artificial Intelligence Job in India

Position Type: Permanent

Role: Artificial Intelligence Engineer

Experience: 3-5 Years

Work Location: Hyderabad / Bangalore / Chennai

Work Mode: Hybrid

Shift Timings: 2:00 PM - 11:00 PM

Job Summary

We are seeking a Senior Data Scientist with strong expertise in Large Language Models (LLMs) and Transformer based architectures to build and scale Generative AI applications for document summarization, classification, and question answering using structured and unstructured data. The role involves hands on development, experimentation, and implementation of advanced AI techniques in a production environment.

Key Responsibilities

• Design and develop LLM powered applications for summarization, classification, and Q&A
• Implement AI safety mechanisms and guardrails at the API layer
• Work on prompt engineering, RAG pipelines, and vector search
• Collaborate with cross functional teams to understand data requirements
• Conduct research and experimentation on emerging Generative AI techniques
• Apply best practices in ML engineering and model deployment

Must Have Skills

• Large Language Models (LLM)
• Generative AI
• Transformers
• Python
• Prompt Engineering
• Retrieval Augmented Generation (RAG)
• LangChain / LlamaIndex
• Vector Databases (Pinecone, ChromaDB, pgvector or similar)
• SQL, Spark
• Git / GitHub

Good to Have Skills

• OpenAI GPT, Google PaLM, LLaMA
• AWS SageMaker, Bedrock
• AI Safety / Responsible AI
• API based AI application development

About the Project

Recro is partnering with StockGro to build Stoxo, a frontier AI platform for the Indian Capital Markets. You will be part of an elite engineering squad tasked with leveraging India's largest financial datasets to build custom LLM/SLM stacks. This role is for engineers who want to move beyond "GPT-wrappers" and build deep, tool-chained AI systems that can analyze stocks, predict trends, and automate complex financial workflows.

The Tech Stack

• Languages: Python (Custom Stack Development)
• AI Orchestration: LangChain, LangGraph, Tool Chaining
• Models: Specialized LLMs & SLMs (Llama 3, Mistral, Phi-3, custom fine-tuned models)
• Backend: FastAPI, Pydantic, Celery
• Data: Vector DBs (Pinecone/Milvus), PostgreSQL, Real-time Market Data Feeds
• Prompting: Advanced System Prompts & Agentic Workflows

Key Responsibilities

• Architecting AI Agents: Design "Tool Chaining" systems where AI can autonomously call financial APIs, browse news, and perform technical analysis.
• Model Optimization: Implement and optimize Small Language Models (SLMs) for high-speed inference in low-latency trading/analysis environments.
• Custom Python Development: Build the core logic for the Stoxo platform, ensuring the AI stack integrates seamlessly with StockGro's existing data infrastructure.
• System Prompt Engineering: Craft and iterate on complex system prompts to ensure the AI maintains a "financial expert" persona with high factual accuracy.
• Collaborative Delivery: Work closely with the Recro project leads and StockGro stakeholders to hit aggressive product milestones.

Required Skills

• Advanced Python: Deep understanding of asynchronous programming and custom package development.
• AI Frameworks: Hands-on experience with LangGraph (or similar state-machine based AI agents) and LlamaIndex.
• Financial Literacy: A strong grasp of capital markets (Stocks, Options, Technical Analysis) is highly preferred.
• Engineering Rigor: Experience with MLOps, version control for prompts, and evaluating LLM outputs for "hallucinations."

Why Join This Project?

• High Impact: Work on a product used by millions of users in India's premier social investment platform.
• Technical Growth: Move from general AI development to specialized Capital Market AI, one of the most lucrative and technically challenging niches.
• Path to Absorption: Successful completion of the initial contract period opens doors for direct permanent roles within the StockGro/Stoxo ecosystem.

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:

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

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

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

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


Recro



2 days ago