Machine Learning Engineer

MLOPS Engineer

• Help data-driven teams build production-ready, scalable machine learning systems.
• Productionize machine learning models.
• Maintain and improve automated ML pipelines and experimentation infrastructure focusing on scalability, usability, and performance.

• 2 full-time weeks of hands-on bootcamp training focusing on deployable code, containerization, ML(ops) engineering, and cloud fundamentals.
• Work 4 days a week as an ML(ops) Engineer at client companies such as Heineken, Rabobank, ASML, Lely, Fed Ex, and Vattenfall.
• Participate in ongoing training and project support from technical leads, including Friday meetings at the Xccelerated office in Amsterdam or remote.
• At the end of the first year, have the opportunity to join the partner organization directly.

As an ML(ops) Engineer you will collaborate with other medior‑ and senior team members on challenging projects for clients, working on data science & ML topics such as predictive software solutions, fraud detection, data engineering challenges including building data pipelines, developing cloud architectures, and product ionizing machine learning models.

You should also enjoy working with big data tools and programming frameworks to ensure raw data gathered from data pipelines are used to build production‑ready, scalable applications driven by data and AI.

• Good salary.
• 25 vacation days.
• Kick‑off with 2 full-time weeks of hands‑on bootcamp training.
• Technical trainings & innovation days for a full year (every week).
• Challenging assignments.
• Mac Book and iPhone.
• Lunches, coffee, and snack bar.
• Flexibility in working from home and at the office.

• Technical bachelor’s or master’s degree (e.g., Data Science, Econometrics, Artificial Intelligence).
• 2‑4 years of work experience as an ML Engineer or Data Scientist.
• Statistical knowledge but mainly experienced with end‑to‑end systems and able to run, deploy, and monitor them.
• Knowledge of open‑source technologies like Spark, Kafka, Airflow, or Kubernetes.