AI Data Engineer

Job Description 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 • 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) LLM / GenAI & Agentic Engineering • Strong hands-on experience with: o LLMs (Claude, OpenAI, etc.) o RAG pipelines and retrieval optimisation o GPT + Agentic AI implementation experience • Experience with: o LangChain, LangGraph, or similar frameworks o Agent orchestration and tool-calling architectures • Deep understanding of: o 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: o Cloud platforms (Azure/AWS/GCP) o SQL o 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 • Exposure to agentic workflows or tool calling concepts • Basic knowledge of fine-tuning / prompt tuning (LoRA, PEFT – optional exposure) • Experience with Azure OpenAI / Azure AI Search or similar stacks • Awareness of enterprise AI considerations (data security, privacy, governance)