Senior Data Scientist_GenAI (LLM, RAG, LangGraph) - Immediate Joiners

Role Overview :

We are seeking a highly skilled Senior Data Scientist with hands-on experience in Generative AI to design, build, and deploy advanced AI solutions. This role involves working on cutting-edge projects like RAG-based chatbots, LLM-driven BI systems, and autonomous coding agents. You will collaborate cross-functionally with engineering teams to turn AI prototypes into scalable production systems.

Key Responsibilities :

Architect and implement end-to-end Generative AI solutions (e.g., RAG chatbots, AI-powered BI tools, code-generating agents)

Develop intelligent agents using LangGraph or similar orchestration frameworks

Build scalable data ingestion and retrieval pipelines leveraging LlamaIndex and related libraries

Design and maintain high-performance vector databases for semantic search and information retrieval

Integrate and benchmark multiple LLM providers (OpenAI, Gemini, Claude, etc.)

Set up monitoring and observability systems using tools like LangSmith for real-time tracking of AI performance

Work closely with engineering teams to productionize machine learning models and applications

Required Skills & Qualifications :

3–7 years total experience, with a minimum of 1 year focused on Generative AI

Generative AI Expertise :

Practical experience with LangGraph or equivalent frameworks for agent orchestration

Strong knowledge of LlamaIndex , LangChain , or similar libraries for document processing

Proficiency with vector databases (Pinecone, Chroma, Weaviate, etc.)

Familiarity with LLM APIs from providers like OpenAI , Anthropic , and Google

Hands-on experience with LLM observability tools such as LangSmith, Weights & Biases

Demonstrated ability to build natural language to SQL BI tools

Proven experience in building RAG systems for contextual chatbot solutions

Experience in developing code generation agents and LLM-based programming assistants

Traditional AI & Machine Learning :

Solid foundation in ML techniques such as classification, regression, clustering, and time-series forecasting

Proficiency with frameworks like scikit-learn , TensorFlow , and PyTorch

Experience in statistical analysis , A / B testing , and experimental design

Strong data handling skills, including feature engineering and preprocessing

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