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
0–4 years total experience, with exposure to AI/ML, NLP, or Data Engineering
projects
Hands-on experience or robust learning exposure to LLM / GenAI use cases
(projects, POCs, academic work, or professional)





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
Robust 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

Good-to-Have
Exposure to agentic workflows or tool calling concepts
Basic knowledge of fine-tuning / prompt tuning (LoRA, PEFT – optional
exposure)
Expe

📌 Senior Executive Chennai (India)
🏢 EXL
📍 India