Data Engineer - Automation & AI

Engagement

Context

• A large, international company in the
• engineering
• sector is scaling production-grade AI across the organization.
• The Data & AI Platform team is expanding delivery capacity and is looking for a contract Data & AI Engineer who can step in immediately, take ownership of AI use cases end-to-end, and accelerate adoption across business teams on an
• Azure + Databricks
• foundation.
• This is a hands-on delivery role with high autonomy: you will build, ship, operate, and improve AI solutions in production—while introducing reusable components and pragmatic best practices that raise the bar across the platform.
• What you’ll do
• **Deliver AI use cases end-to-end**
• : from ingestion and feature engineering to model/agent development and production rollout.
• **Design and operate Databricks lakehouse pipelines**
• (batch and streaming) using Spark/SQL/Delta Lake, including monitoring and data quality controls.
• **Build AI solutions on the platform**
• , including:
• RAG patterns (retrieval, chunking, embeddings, evaluation)
• tool-using agents and orchestration approaches
• prompt strategies and testing/guardrails
• (where relevant) custom ML models and supporting pipelines
• **Productionize and run what you build**
• : reliability, observability, cost control, and operational hygiene.
• **Enable other teams**
• by creating reusable components, templates, and delivery standards.
• **Work with governance and compliance**
• : align with AI governance requirements and ensure solutions are secure and auditable.
• **Collaborate with stakeholders**
• across IT and the business to translate needs into working solutions and clear delivery increments.
• What success looks like (first weeks)
• Rapidly understand the current Azure/Databricks landscape and delivery priorities.
• Pick up 1–2 active use cases and move them toward production-quality standards.
• Strengthen delivery patterns (templates, evaluation approach, monitoring, data quality checks).
• Create momentum with visible working increments and pragmatic documentation.
• Required experience
• Proven experience as a

Data Engineer / Data & AI Engineer

• delivering solutions into production environments.
• Strong hands-on

Databricks

• expertise: Spark/SQL, Delta Lake, Jobs/Workflows, performance tuning.
• Strong
• Python + SQL
• for data engineering and AI/ML workflows.
• Experience building
• data pipelines
• with quality checks and operational monitoring.
• Practical experience with
• LLM-based solutions
• (RAG and/or agents), including prompt strategies and evaluation approaches.
• Comfortable working independently in an interim context: you can
• own delivery
• , communicate clearly, and unblock yourself.
• Nice to have
• Azure services exposure (e.g., Azure ML, Azure OpenAI, Key Vault, Functions, ADF).
• LLM toolkits (LangChain, Semantic Kernel), prompt evaluation frameworks, early LLMOps patterns.
• CI/CD (GitHub Actions) and Infrastructure-as-Code (Terraform).
• ML frameworks (PyTorch, TensorFlow, scikit-learn) where needed.
• Why this assignment
• Immediate impact: deliver AI use cases into production on a modern Azure Databricks platform.
• High ownership and autonomy: a true interim role where delivery outcomes matter.
• Real-world relevance: projects tied to large-scale operations in a complex, safety- and compliance-aware environment.