Key Responsibilities - Design and develop LLM-powered applications using agentic patterns (single/multi-agent) for business use cases

- Build and optimise end-to-end RAG pipelines (ingestion, embeddings, retrieval, orchestration, response synthesis)
- Implement prompt engineering and orchestration techniques (prompt chaining, tool/function calling, structured outputs)
- Develop production-grade APIs and services (FastAPI/Flask/Streamlit) for GenAI applications
- Integrate LLM solutions with enterprise systems, data platforms, and workflows
- Apply guardrails and evaluation frameworks to improve response quality, reduce hallucinations, and ensure responsible AI usage
- Collaborate with Data Engineering and MLOps teams for data pipelines, deployment, monitoring, and scaling
- Contribute to reusable components, documentation, and engineering best practices Experience & Core Requirements (Must-Have) Overall Experience - 6–9 years total experience
- 1–3+ years in hands-on GenAI / LLM application development (production use cases) LLM / GenAI & Agentic Engineering - Strong hands-on experience with:
- LLMs (Claude, OpenAI, etc.)
- RAG pipelines and retrieval optimisation
- GPT + Agentic AI implementation experience
- Experience with:
- LangChain,



LangGraph, or similar frameworks
- Agent orchestration and tool-calling architectures
- Deep understanding of:
- LLM limitations, evaluation, and optimisation strategies Core Engineering - Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience
- Deep data analysis experience and handling large volume of data
- Fabric/Azure Databricks/Snowflake data engineering integration skills
- Good exposure to:
- Cloud platforms (Azure/AWS/GCP)
- SQL
- Containers, CI/CD, monitoring Data / AI Foundations (Mandatory) Prior Experience In One Or More - Data Engineering (ETL/ELT, pipelines, orchestration)
- Data Science / ML lifecycle (especially NLP)
- Analytics engineering / data products Positive-to-Have / Preferred - Experience with fine-tuning techniques (LoRA, PEFT) or prompt tuning strategies
- Experience with enterprise GenAI security & privacy practices (data masking, access control, compliance)
- Familiarity with Azure AI ecosystem (Azure OpenAI, Azure AI Search, Fabric, etc.)
- Exposure to agentic coding tools (e.g., Claude Code or similar environments)

📌 Senior AI Data Engineer (Noida)
🏢 EXL
📍 Noida