job description
about the role
we are looking for an ai engineer with experience in speech-to-text and text generation to solve a conversational ai challenge for our client based in emea. the focus of this project is to transcribe conversations and leverage generative ai-powered text analytics to drive better engagement strategies and decision-making.
the ideal candidate will have deep expertise in speech-to-text (stt), natural language processing (nlp), large language models (llms), and conversational ai systems. this role involves working on real-time transcription, intent analysis, sentiment analysis, summarization, and decision-support tools.
key responsibilities
1. conversational ai & call transcription development
- develop and fine-tune automatic speech recognition (asr) models
- implement language model fine-tuning for industry-specific language.
- develop speaker diarylation techniques to distinguish speakers in multi-speaker conversations.
2. nlp & generative ai applications
- build summarization models to extract key insights from conversations.
- implement named entity recognition (ner) to identify key topics.
- apply llms for conversation analytics and context-aware recommendations.
- design custom rag (retrieval-augmented generation) pipelines to enrich call summaries with external knowledge.
3. sentiment analysis & decision support
- develop sentiment and intent classification models.
- create predictive models that suggest next-best actions based on call content, engagement levels, and historical data.
4. ai deployment & scalability
- deploy ai models using tools like aws, gcp, azure ai, ensuring scalability and real-time processing.
- optimize inference pipelines using onnx, tensorrt, or triton for cost-effective model serving.
- implement mlops workflows to continuously improve model performance with new call data.
qualifications
technical skills
- strong experience in speech-to-text (asr), nlp, and conversational ai.
- hands-on expertise with tools like whisper, deepspeech, kaldi, aws transcribe, google speech-to-text.
- proficiency in python, pytorch, tensorflow, hugging face transformers.
- experience with llm fine-tuning, rag-based architectures, and langchain.
- hands-on experience with vector databases (faiss, pinecone, weaviate, chromadb) for knowledge retrieval.
- experience deploying ai models using docker, kubernetes, fastapi, flask.
soft skills
- ability to translate ai insights into business impact.
- strong problem-solving skills and ability to work in a fast-paced ai-first environment.
- excellent communication skills to collaborate with cross-functional teams, including data scientists, engineers, and client stakeholders.
preferred qualifications
- experience in healthcare, pharma, or life sciences nlp use cases.
- background in knowledge graphs, prompt engineering, and multimodal ai.
- experience with reinforcement learning (rlhf) for improving conversation models.
