Data & AI Engineer Rotterdam
Summary
A hands-on Data & AI Engineer role at consultancy Sytac, embedded with a marine/engineering-sector client's Data & AI Platform team. Day to day you build production AI solutions on Azure + Databricks: lakehouse data pipelines (batch/streaming), RAG and tool-using LLM agents, and reusable AI components, using Python and SQL.
At Sytac, we build high-performing engineering teams for leading organizations in the Netherlands and beyond. We combine a pragmatic, people-first culture with strong technical craftsmanship — giving engineers autonomy in real production environments, backed by a consultancy that invests in growth, community, and long-term partnerships.
For one of our key clients in the marine and engineering sector, we are looking for a Data & AI Engineer to join a dedicated Data & AI Platform team. You will be responsible for delivering production-grade AI solutions, designing intelligent agents, and building the data foundations that accelerate AI adoption across a global organization.
This is a hands-on role built on an Azure + Databricks foundation, offering a clear growth path for an engineer eager to bridge the gap between core data engineering and cutting-edge Generative AI.
What you’ll do
Deliver end-to-end AI use cases, including data pipelines, feature sets, models, and intelligent agents.
Build and operate Databricks lakehouse pipelines (batch and streaming) with integrated data quality checks.
Engineer advanced AI solutions, focusing on RAG (Retrieval-Augmented Generation), tool-using agents, and prompt strategies.
Enable business teams by creating reusable components, templates, and best practices for AI development.
Ensure operational excellence, maintaining reliability, cost control, and compliance with AI governance standards.
Develop custom models and prompts tailored to specific engineering and business challenges.
Collaborate across the organization to translate complex requirements into scalable, production-ready AI products.
What we’re looking for
Academic Foundation: Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
Technical Proficiency: Strong hands‑on experience with Python and SQL for both data engineering and machine learning.
Databricks Expertise: Solid understanding of the Databricks ecosystem (Spark, Delta Lake, and Workflows).
Project Portfolio: A demonstratable portfolio of projects (academic, internship, or professional) showcasing your ability to build and deploy data/AI solutions.
Proactive Learner: A team-first mindset with a drive to stay ahead of rapidly evolving AI trends.
Tooling (must understand and use in practice): Databricks (Spark/SQL), Azure Cloud, Python, Delta Lake, and Git.
Nice to have
Azure AI Stack: Experience with Azure OpenAI and Azure Machine Learning services.
LLM Toolkits: Familiarity with frameworks like LangChain or Semantic Kernel, and an understanding of LLMOps.
DevOps/IaC: Experience with GitHub Actions and Terraform.
ML Frameworks: Experience with PyTorch, TensorFlow, or scikit‑learn.
Skills
- AI
- Ai Enablement
- Azure
- Cloud
- Data Engineering
- Data Pipelines
- Data Quality
- Data Science
- Databricks
- Delta Lake
- DevOps
- Generative AI
- Git
- GitHub
- GitHub Actions
- Infrastructure as Code
- Lakehouse
- LangChain
- LLM
- LLMOps
- Machine Learning
- OpenAI
- Python
- PyTorch
- RAG
- scikit-learn
- Semantic Kernel
- Spark
- SQL
- TensorFlow
- Terraform