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.

2–4 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)
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
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
Prior experience in one or more: Data Engineering (ETL/ELT, pipelines, orchestration)
Data Science / ML lifecycle (especially NLP)
Analytics engineering / data products

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.

Associate AI Data Engineer

Key skills