As an ML / AI Engineer at DataAxis you turn models and AI prototypes into dependable production systems for our clients. That spans classical ML — training pipelines, serving, and MLOps — through to modern LLM and generative-AI applications, including private, self-hosted deployments for clients who cannot send data to a public AI cloud.
What you will do
- Build reproducible training and inference pipelines and deploy models to production
- Implement MLOps: experiment tracking, model registries, CI/CD, monitoring, and drift detection
- Migrate and re-platform models onto modern stacks (e.g. to Python on Databricks)
- Build LLM and RAG applications grounded in client data, with evaluation and guardrails
- Deploy open models (Llama, Mistral) privately inside client cloud/VPC or on-prem environments
- Work hands-on with clients and translate their problems into robust solutions
What you bring
- 3+ years building ML or AI systems in production (not just notebooks)
- Strong Python; experience with PySpark, MLflow, or similar tooling
- Solid software-engineering fundamentals: testing, packaging, CI/CD
- Familiarity with LLM/RAG patterns, vector databases, and evaluation
- A pragmatic focus on reliability, latency, and cost
- Fluent English and strong communication; comfortable working with clients
- Based in or willing to work from the Netherlands (EU work authorization)
Nice to have
- Experience deploying self-hosted / open-weight LLMs
- Cloud or Databricks certifications
- Data-engineering experience (we value people who can build the full pipeline)
- Dutch language
What we offer
- Work across the full spectrum — classical ML to cutting-edge generative AI
- A technical team that ships real systems for recognised clients
- Flexible, hybrid working from Delft and client sites
- Competitive compensation and genuine ownership of your work