Key Responsibilities

- Design and develop LLM-based solutions for business use cases (e.g., chatbots, summarisation, document intelligence).

- Build and optimise RAG (Retrieval Augmented Generation) pipelines including data ingestion, embeddings, and retrieval.

- Implement prompt engineering techniques (prompt design, chaining, optimisation).

- Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit).

- Integrate LLM solutions with enterprise systems and structured/unstructured data sources.

- Apply basic guardrails and evaluation techniques to improve response quality and reduce hallucinations.

- Collaborate with cross-functional teams to ensure data quality, model performance, and deployment readiness.

- Document solutions and contribute to reusable components and best practices.

Must-Have Skills

Experience

- 2–4 years total experience, with exposure to AI/ML, NLP, or Data Engineering projects

- Hands-on experience or solid learning exposure to LLM / GenAI use cases (projects, POCs, academic work, or professional)

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

Good-to-Have

- Exposure to agentic workflows or tool calling concepts

- Basic knowledge of fine-tuning / prompt tuning (LoRA, PEFT – optional exposure)

- Experience with Azure OpenAI / Azure AI Search or similar stacks

- Awareness of enterprise AI considerations (data security, privacy, governance)

📌 Associate AI Data Engineer (Pune)
🏢 EXL
📍 Pune