Senior AI/GenAI Python Engineer

About the role

We are looking for a hands‑on Senior AI / GenAI Engineer who combines strong Python engineering skills with practical experience building production‑oriented Retrieval‑Augmented Generation (RAG) and agentic AI solutions. The role requires taking full ownership of problems, defining sensible technical approaches, and delivering end‑to‑end implementations without continuous direction.

Key responsibilities

• Design, develop, and maintain production‑grade Python APIs (FastAPI preferred) and end‑to‑end RAG pipelines, including data ingestion, retrieval, context preparation, and response generation.
• Build and orchestrate agent‑based AI workflows using frameworks such as LangGraph, integrating large language models with tool‑calling capabilities.
• Implement and optimise graph database solutions (e.g., Neo4j) and Azure cloud services such as Azure AI Search, Cosmos DB, Document Intelligence, and Application Insights.
• Collaborate with business stakeholders to understand objectives, evaluate cost and operational implications, and drive technical decisions from concept to deployment.

Required profile

• Strong engineering ownership – able to independently initiate, design, and deliver solutions.
• Ability to translate business problems into technical specifications while considering budget and scalability.
• Proactive mindset to challenge existing approaches and propose better alternatives.
• Demonstrated passion for AI, with personal projects or experiments that showcase real‑world impact.

Required skills

• Python programming with solid OOP and software engineering principles.
• FastAPI (or similar) for API development.
• Experience with RAG techniques: hybrid search, reranking, query rewriting, and evaluation frameworks like RAGAS or TruLens.
• Agentic AI workflow orchestration using LangGraph or equivalent.
• Graph databases such as Neo4j.
• Azure cloud services (AI Search, Cosmos DB, Document Intelligence, AI Foundry, Application Insights).
• Familiarity with data‑engineering tools (Apache Airflow, PySpark, Azure Data Factory) is a plus.