Key Responsibilities

- Design and develop LLM-based applications using single-agent or simple multi-agent patterns for business use cases

- Build and maintain RAG pipelines: data ingestion → chunking → embeddings → retrieval → response generation

- Implement prompt engineering techniques (prompt templates, chaining, basic tool/function calling)

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

- Integrate AI solutions with enterprise systems, databases, and APIs

- Apply basic guardrails and validation checks to improve response quality and reduce hallucination

- Work with Data Engineering teams to ensure data quality, pipeline efficiency, and proper documentation

- Collaborate with MLOps teams for deployment, monitoring, and iterative improvements

- Document solutions, reusable components, and best practices

Must-Have Skills

Experience

- 4–6 years total experience, with 1 year hands-on experience in GenAI / LLM-based applications

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

- Solid 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 / Preferred

- Exposure to model fine-tuning (LoRA/PEFT) or prompt optimisation techniques

- Experience with evaluation of LLM outputs (quality, relevance, latency)

- Understanding of enterprise data privacy and security considerations in GenAI

- Exposure to Azure AI / Azure OpenAI / AI Search ecosystems

- Experience working on real client-facing AI solutions or POCs

📌 AI Data Engineer (Noida)
🏢 EXL
📍 Noida