Grid Dynamics

2 days ago

Bengaluru, Karnataka, India GRID DYNAMICS PRIVATE LIMITED Full time ₹ 20,00,000 - ₹ 25,00,000 per year

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 :

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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