About the role

Job Description

Job Title: Data Scientist - GenAI

Location: Gurgaon

Work Experience: 5+ 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).