Machine Learning Engineer / MLOps Engineer (GCP)

Location: Hybrid

Duration: 6 months contract

About the Role

We are building a scalable Machine Learning foundation on Google Cloud Platform (GCP) to standardize and operationalize ML across the organization. As an MLOps Engineer, you will design and implement the infrastructure and processes that enable fast, reliable, and production-ready ML solutions.

Key Responsibilities

Build end-to-end ML pipelines (training, deployment, monitoring)
Develop and maintain CI/CD pipelines for ML workflows
Manage the full ML lifecycle (versioning, retraining, monitoring)
Design scalable ML architecture on GCP
Create reusable, standardized components
Collaborate with data scientists and data engineers to operationalize models
Document and transfer knowledge of existing solutions

Requirements

Must-have:

Experience as an ML Engineer and/or MLOps Engineer
Strong knowledge of MLOps practices
Hands-on experience with GCP
Experience with ML in production environments
Ability to design scalable, reusable solutions

Nice-to-have:

Understanding of data science workflows
Experience building ML platforms
Experience in complex enterprise environment

Contact olivia.zimmerman@empiric.com for more information.

Empiric

Vacatures

Machine Learning Engineer