SENIOR MACHINE LEARNING ENGINEER - AMSTERDAM - 6 MONTHS
Role details
Job location
Tech stack
Job description
Global Enterprise Partners is currently looking for a Senior Machine Learning engineer for an initial 6 months project with our client in Amsterdam. This role requires 3 days on site presence in Amsterdam
Purpose of the Role The Machine Learning Engineer/MLOps Engineer is responsible for designing, building, and standardizing the ML infrastructure and processes that support data scientists and enable scalable, production ready machine learning solutions.
The goal is to create a solid ML foundation that allows the team to: Develop and deploy models faster Reuse standardized components Improve reliability, monitoring, and governance Scale ML solutions sustainably across teams
Key Responsibilities MLOps & Machine Learning Engineering Design and implement end to end ML pipelines (training, validation, deployment, monitoring). Build and maintain CI/CD pipelines for machine learning models. Manage the full ML life cycle, including versioning, monitoring, and retraining. Automate and standardize ML workflows to increase team efficiency.
Required Skills & Experience
Must have Proven experience as a Machine Learning Engineer and/or MLOps Engineer. Strong understanding of MLOps principles (pipelines, deployment, monitoring). Hands on experience with Google Cloud Platform (GCP) is required. Experience with ML architecture and production environments. Ability to design scalable, standardized, and reusable solutions.
Nice to have Basic understanding of Data Science processes and workflows. Experience building ML platforms in a growth or transformation phase. Experience in complex enterprise environments.
Are you interested in this opportunity and do you meet the requirements? Please get in touch with Marco Eindhoven of Global Enterprise Partners
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Requirements
Must have Proven experience as a Machine Learning Engineer and/or MLOps Engineer. Strong understanding of MLOps principles (pipelines, deployment, monitoring). Hands on experience with Google Cloud Platform (GCP) is required. Experience with ML architecture and production environments. Ability to design scalable, standardized, and reusable solutions.
Nice to have Basic understanding of Data Science processes and workflows. Experience building ML platforms in a growth or transformation phase. Experience in complex enterprise environments.