Principal AI Data Engineer.

Contract role: Principal AI Data Engineer

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.
• Strong 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.