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, Mosiac 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 Required Skills • Bachelor or Master or equivalent in Statistics, Mathematics, Econometrics or similar discipline with at least 8-12 years’ experience on data science/AI projects. • Deep understanding of LLM families (GPT, Llama, Claude, Mistral) and their reasoning capabilities. • S trong experience with Databricks- DLT, Delta Lake concepts, UC governance. • Solid understanding of streaming technologies (e.g., Spark Structured Streaming, Autoloader) • Programming skills in Python, SQL, or Scala. • Proficiency in data modelling, ETL/ELT processes, and data architecture. • Strong analytical background with problem-solving skills. • Performance tuning concepts like watermarking, late data handling, parallelism & checkpointing. • Hands-on expertise in ADF, and Qlik Replicate for data ingestion and replication. • Experience working in Azure cloud environments. • Experience with GenAI evaluation frameworks and benchmarking methodologies. • Experience in MS Copilot, AI Foundry , Databricks (MosiacAI, MLflow, Agentbricks, Genie) • Strong Git practices and collaborative coding standards. • A passion for and expertise in practicing data science to solve real-world problems. • Excellent oral and written communication skills. • Strong interpersonal skills and enthusiasm for teamwork, as well as the ability to work independently. • Familiarity with the enterprise AI platforms and governance models is a plus. • Strong decision-making abilities, using data-driven insights to make informed choices that align with organizational goals. • Skills in managing conflicts and facilitating effective resolutions to maintain a positive and productive team dynamic. • Ability to engage with and manage expectations of various stakeholders, including executives, project managers, and other teams. • Proficiency in identifying potential risks in data projects and implementing strategies to mitigate them. • Strong commitment and ownership of project delivery.