AI & Data Engineer [Interim]

Overview

Job Title: AI & Data Engineer [Interim]

Location: Hybrid, Rotterdam

Engagement: Full-time, 5 days a week

Contract duration: 3-6 months (possible extension)

Start date: ASAP

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 an interim 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.

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