Principal AI Data Engineer

Contract Role: Principal AI Data Engineer

Contract Location: London, 5 days onsite weekly

Contract Start Date: August 2026

Contract Duration: 4 months

Payroll provider: Rockford Payroll Info for Contingent Workers – Rockford Pay

Job Description:

Key Responsibilities

• Develop and evaluate AI/GenAI/AgenticAI prototypes using tools like Copilot Studio, AI Foundry and Copilot Analyst Agent, Mosaic AI, Genie, AgentBricks, MLflow with a focus on quick wins and enterprise integration.
• Build and tune Retrieval-Augmented Generation (RAG) systems, including embedding model selection, prompt engineering, and traceable evaluation.
• Design and deploy basic AI agents using frameworks such as LangChain, AutoGen, and smolagents.
• Communicate complex AI concepts clearly to business stakeholders and cross-functional teams.
• Collaborate on E platform enhancements and work within its current limitations.
• Deploy models and applications using Azure OpenAI, Azure AI Foundry, Databricks Mosaic Gateway, and Docker.
• Follow DevOps best practices including CI/CD pipelines, testing, linting, and GitHub workflows.
• Write modular, reusable code using OOP design patterns in Python (Pydantic, PyTorch, etc.).
• Operate in agile teams and contribute to sprint planning, reviews, and retrospectives.
• Deliver hands-on GenAI/AgenticAI systems used directly by commercial teams within Trading & Supply, taking solutions from prototype to production.
• Apply engineering skills (emphasis on Databricks) and research skills across experimentation, rapid prototyping, and iterative delivery. Someone who puts emphasis on reproducibility and open source, manages large-scale text and structured datasets on Databricks.
• Build AI capability, manage stakeholders and communicate effectively to ensure alignment between business needs and AI solutions, and a quick understanding of commercial operations that happen in T&S.
• Design and run evaluation and testing frameworks for GenAI systems, including benchmarking, reproducibility checks, and structured model assessments.
• Build solutions using Databricks infrastructure, Genie, MLflow (deployment and tracing and evaluations), LangChain, and LangGraph, and integrate them into scalable AI workflows and architectures.
• Contribute to system planning, architectural design, and structured testing to ensure long-term reliability, performance, and maintainability.
• Preferably also someone who can set the building blocks and lead building out the backlog.