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.
