MLOps Engineer
Listed on 2026-09-18
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IT/Tech
Machine Learning/ ML Engineer, AI Engineer (Applied/Software), Data Engineering, Cloud Computing: Infrastructure & Operations
(MLOps Engineer) As an MLOps Engineer, you build the bridge between data science and production environments by deploying, monitoring, and maintaining machine learning models. You develop CI/CD pipelines specifically for ML workflows, manage feature stores, and ensure that models perform reliably in production. You work closely with data scientists to make their models production-ready and with Dev Ops teams to optimize infrastructure.
Additionally, you continuously monitor model performance, detect model drift, and implement automated retraining pipelines to ensure quality.
For this position, you need at least an HBO bachelor's degree in computer science, data science, software engineering, or a comparable technical field. The N&T specialization profile in secondary school provides the best preparation through its focus on mathematics, physics, and computer science. Subjects like programming, statistics, and cloud computing are essential. A WO master's degree can be an advantage, but extensive practical experience with ML deployment and Dev Ops tools often compensates for a lower educational level.
RequiredSkills
You often start as a Data Engineer or Software Engineer with an interest in machine learning, where you gain experience with data pipelines and deployment. You then grow into an MLOps Engineer role, where you specialize in ML-specific infrastructure. With more experience, you can advance to Senior MLOps Engineer or ML Platform Architect, where you design enterprise-wide ML platforms. Eventually, you can become Head of ML Engineering or specialize as an ML Infrastructure Consultant for various organizations.
AIImpact on the job
Paradoxically, as an MLOps Engineer, you actually benefit from the AI revolution because more ML models need to be brought into production. AI tools do help you automate routine deployment tasks and generate monitoring dashboards. Your work shifts toward more complex challenges such as multi-model orchestration, real-time inference optimization, and responsible AI deployment. The demand for your expertise grows exponentially as organizations realize their AI ambitions.
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