Back
Remote role and mid-level experience attract broader applicants despite MLOps specialization.
Requires specialized MLOps, GCP, and model-serving skills limiting cross-industry transferability.
Explicit 6+ years requirement plus mandatory GCP/Vertex AI and MLOps tooling increases filtering strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead and manage the deployment, maintenance, monitoring, and support of AI/ML models in production on Google Cloud Platform (GCP).
Architect, build, and maintain scalable MLOps pipelines focusing on GCP services like Vertex AI, GKE, Cloud Storage, BigQuery, and CI/CD automation for ML model deployment.
Manage data cataloging tools for versioning, lineage tracking, governance, and documentation of datasets and ML models ensuring traceability and reusability.
Minimum 6 years of professional experience in MLOps roles with at least 2 years in project leadership.
University degree in Mathematics, Statistics, Computer Science, Physics, or similar field.
Proven hands-on experience deploying, monitoring, and managing ML models on Google Cloud Platform using services such as Vertex AI, GKE, BigQuery, Cloud Build, and familiarity with model serving and MLOps frameworks.
Experience with data catalog tools and strong knowledge of GCP infrastructure; formal GCP certification is a significant advantage.
Experienced in leading and hands-on execution of MLOps projects within international or cross-functional teams.
Strong technical expertise in GCP ML infrastructure and associated tools indicating deep specialization in cloud-native machine learning operations.
Skilled in building automated monitoring, logging, and versioning pipelines that support scalable and maintainable ML deployments and governance.