Machine Learning Engineer

As a Machine Learning Engineer at Xomnia, you will build AI systems that make it out of the notebook. Feature pipelines, model serving, evaluation, monitoring, CI/CD: the engineering that turns a promising model into something a client’s team actually uses every day. Part of that work is classical ML. A growing part of it is LLM-based: RAG pipelines, agentic workflows, and GenAI applications running in production. You do this as a consultant, which means you carry both the engineering and the conversation with the people who have to work with what you build. See yourself doing this? Then read along! What you will work on Three recent projects from our AI unit:

• Enexis : a forecasting model that predicts which excavation works risk damaging underground cables. We took it from a standing prototype to the central tool the prevention team works with.
• Sea Turtle Conservation Bonaire : a computer vision model that identifies individual sea turtles by their facial pattern, so researchers can track them without invasive tagging.

• KLM : two delay prediction models, built and deployed on Kubernetes with a CI/CD setup that lets the team ship an improvement in minutes. The models produce around 18.000 predictions a month and feed the tools that decide whether to swap an aircraft or reschedule a flight. Our AI work concentrates in a couple of verticals, such as energy & utilities, finance & insurance and AI for Good. At VodafoneZiggo we run GenAI applications across several customer service use cases, with our GenAI Lead stepping in as a technically focused Product Owner. Job requirements Your role as MLE

• Take models from experiment to production: feature engineering, data pipelines, model serving, monitoring, and CI/CD.
• Build GenAI applications that hold up in production: retrieval, agentic workflows, evaluation sets, guardrails, and cost and latency budgets that the client can live with.
• Choose the right tool for the problem. Sometimes that is an LLM. Often a forecasting model, a recommender or an anomaly detector is the better answer, and part of your job is saying so.
• Deploy on Azure, AWS or GCP, using managed AI platforms such as AWS Bedrock or Microsoft AI Foundry where they fit.
• Work with the client team, not next to it: translate what the business needs into something buildable, and explain the trade-offs you made.
• Help clients build their own capability, through code reviews, pairing and engineering practices that outlast your assignment.
• Share what you learn internally through Learning Labs, Xpert sessions and mentoring. What you bring to the table

• 3+ years of experience in ML engineering, AI engineering, data science or a comparable role.
• Strong Python, and solid command of at least one of scikit-learn, PyTorch or TensorFlow.
• Hands-on experience with LLMs beyond the API call: RAG, vector stores, agent frameworks such as LangChain or LlamaIndex, and evaluating output quality rather than assuming it.
• Comfortable with production tooling: MLflow, Airflow, Docker, Kubernetes, Databricks.
• A degree in Computer Science, AI, Data Science or a related technical field.
• A consulting mindset: you listen first, ask the awkward question early, and turn a vague need into something concrete.
• Fluent in English and/or Dutch. What you can expect from us

• A monthly salary between €4.300 and €6.500 (based on 40 hours, excluding vacation allowance)
• €5.000 annual development budget
• 25 vacation days
• Pension, flexible working location, and the option to take unpaid leave
• Company trips every year (we went to Berlin and Saalbach-Hinterglemm last year)
• Stock Appreciation Rights, so you grow along with our succes You will be one of roughly 120 people who genuinely like this stuff. On the last Friday of every month we close the week with a Learning Lab. In September we run Health Month. When the weather is good we take our own boat out onto the canals, straight from the office. There is a healthy lunch every day and a bar for afterwards. Hear it from our engineers We sat down with Ilse and Fried to talk about their work as MLEs at Xomnia. Read: a lot of learning and much fun along the way. Watch the full video below. At Xomnia, we believe in using data and AI to tackle real-world challenges. With a team of passionate experts from 20+ nationalities and over 30% female representation, we celebrate diversity and welcome different perspectives. We are proud of our core values: We are challengers, we embrace agility, and we are always sharing. Want to be part of this and take the next step in your career? Apply now or reach out to our : .