Principal AI Data Engineer

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London, United Kingdom | Posted on 30/07/2026

Contract role: Principal AI Data Engineer Contract Location: London, 5 days onsite weekly\n

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• Develop and evaluate AI/GenAI/AgenticAI prototypesusing tools like Copilot Studio, AI Foundry and Copilot Analyst Agent, MosiacAI, Genie, AgentBricks, MLflow with a focus on quick wins and enterpriseintegration.
  
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• Build and tune Retrieval-Augmented Generation (RAG)systems, including embedding model selection, prompt engineering, and traceableevaluation.
  
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• Design and deploy basic AI agents using frameworkssuch as LangChain, AutoGen, and smolagents
  
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• Communicate complex AI concepts clearly to businessstakeholders and cross-functional teams.
  
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• Collaborate on E platform enhancements and work withinits current limitations.
  
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• Deploy models and applications using Azure OpenAI, AzureAI Foundry, Databricks Mosaic Gateway, and Docker.
  
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• Follow DevOps best practices including CI/CDpipelines, testing, linting, and GitHub workflows.
  
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• Operate in agile teams and contribute to sprintplanning, reviews, and retrospectives.
  
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• Deliver hands on GenAI/AgenticAI systems used directlyby commercial teams within Trading & Supply, taking solutions fromprototype to production
  
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• Apply engineering skills (emphasis on Databricks) andresearch skills across experimentation, rapid prototyping, and iterativedelivery. Someone who puts emphasis on reproducibility and open source, manageslarge-scale text and structured datasets on Databricks.
  
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• Build AI capability, manage stakeholders andcommunicate effectively to ensure alignment between business needs and AIsolutions, and a quick understanding of commercial operations that happen inT&S
  
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• Design and run evaluation and testing frameworks forGenAI systems, including benchmarking, reproducibility checks, and structuredmodel assessments
  
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• Build solutions using Databricks infrastructure,Genie, MLflow (deployment and tracing and evaluations), LangChain, andLangGraph, and integrate them into scalable AI workflows and architectures
  
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• Contribute to system planning, architectural design,and structured testing to ensure long term reliability, performance, andmaintainability
  
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• Preferably also someone who can set the buildingblocks and lead building out the backlog
  
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Required Skills\n

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