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 strong 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
* 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
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)
🏢 EXL
📍 Chennai