About this role
Job Title: Data Scientist - GenAI
Location: India
Work Experience: 6+ Years
Job Summary:
We are looking for a highly capable and innovative Data Scientist with experience in Generative AI to join our Data Science Team. You will lead the development and deployment of GenAI solutions, including LLM-based applications, prompt engineering, fine-tuning, embeddings, and retrieval-augmented generation (RAG) for enterprise use cases.
The ideal candidate has a strong foundation in machine learning and NLP, with hands-on experience in modern GenAI tools and frameworks such as OpenAI, LangChain, Hugging Face, Vertex AI, Bedrock, or similar.
Key Responsibilities:
• Design and build Generative AI solutions using Large Language Models (LLMs) for business problems across domains like customer service, document automation, summarization, and knowledge retrieval. • Fine-tune or adapt foundation models using domain-specific data. • Implement RAG pipelines, embedding models, vector databases (e.g., FAISS, Pinecone, ChromaDB). • Collaborate with data engineers, MLOps, and product teams to build end-to-end AI applications and APIs. • Develop custom prompts and prompt chains using tools like LangChain, LlamaIndex, PromptFlow, or custom frameworks. • Evaluate model performance, mitigate bias, and optimize accuracy, latency, and cost. • Stay up to date with the latest trends in LLMs, transformers, and GenAI architecture.
Required Skills:
• 5+ years of experience in Data Science / ML, with 1+ year hands-on in LLMs / GenAI projects. • Strong Python programming skills, especially in libraries such as Transformers, LangChain, scikit-learn, PyTorch, or TensorFlow. • Experience with OpenAI (GPT-4), Claude, Mistral, LLaMA, or similar models. • Knowledge of vector search, embedding models (e.g., BERT, Sentence Transformers), and semantic search techniques. • Ability to build scalable AI workflows and deploy them via APIs or web apps (e.g., FastAPI, Streamlit, Flask). • Familiarity with cloud platforms (AWS/GCP/Azure) and MLOps best practices. • Excellent communication skills with the ability to translate technical solutions into business impact.
Preferred Qualifications:
• Experience with prompt tuning, few-shot learning, or LoRA-based fine-tuning. • Knowledge of data privacy and security considerations in GenAI applications. • Familiarity with enterprise architecture, SDLC, or building GenAI use cases in regulated domains (e.g., finance, insurance, healthcare).
Key Responsibilities:
• Design and build Generative AI solutions using Large Language Models (LLMs) for business problems across domains like customer service, document automation, summarization, and knowledge retrieval. • Fine-tune or adapt foundation models using domain-specific data. • Implement RAG pipelines, embedding models, vector databases (e.g., FAISS, Pinecone, ChromaDB). • Collaborate with data engineers, MLOps, and product teams to build end-to-end AI applications and APIs. • Develop custom prompts and prompt chains using tools like LangChain, LlamaIndex, PromptFlow, or custom frameworks. • Evaluate model performance, mitigate bias, and optimize accuracy, latency, and cost. • Stay up to date with the latest trends in LLMs, transformers, and GenAI architecture.
Preferred Qualifications:
• Experience with prompt tuning, few-shot learning, or LoRA-based fine-tuning. • Knowledge of data privacy and security considerations in GenAI applications. • Familiarity with enterprise architecture, SDLC, or building GenAI use cases in regulated domains (e.g., finance, insurance, healthcare).