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
* Robust 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 / d
📌 Senior AI Data Engineer (Gurugram)
🏢 EXL
📍 Gurugram