MLOps Engineer – Google Cloud Platform (GCP)

About the role

We are seeking an experienced MLOps Engineer with deep expertise in Google Cloud Platform to design, deploy, and operate production‑grade AI/ML solutions. The role focuses on building reliable, secure, and automated end‑to‑end machine‑learning platforms that enable seamless collaboration between data scientists, AI engineers, and operations teams.

Key responsibilities

• Design and implement scalable MLOps architectures using GCP‑native services, especially Vertex AI.
• Build standardized frameworks for model training, deployment, monitoring, retraining, and governance.
• Deploy and manage ML models with Vertex AI Endpoints for online and batch inference, including versioning, rollout/rollback, and traffic splitting.
• Create and automate CI/CD pipelines for ML workflows and model releases.
• Develop reproducible ML pipelines with Vertex AI Pipelines across development, testing, and production environments.
• Integrate source control, testing frameworks, and artifact repositories into the ML lifecycle.
• Monitor model performance, drift, data quality, and system reliability; implement observability, logging, alerting, and SLOs.
• Define retraining triggers and support incident analysis and remediation of production ML services.
• Ensure solutions meet scalability, security, compliance, and enterprise architecture standards.
• Collaborate closely with data scientists, AI engineers, data engineers, platform teams, and business stakeholders.

Required profile

• 5+ years of experience in ML engineering, DevOps, or MLOps.
• Minimum 3 years of hands‑on experience with Google Cloud Platform (mandatory).
• Bachelor’s degree in Computer Science, AI, Data Science or related field; GCP certifications are a strong plus.

Required skills

• Google Cloud Platform (GCP) – extensive hands‑on experience.
• Vertex AI (Pipelines, Endpoints, Model Registry, Monitoring).
• CI/CD practices and infrastructure automation.
• Docker and container orchestration concepts.
• Python programming for ML workflows and automation.
• ML monitoring, observability, reliability, and scalability.
• Model versioning, deployment automation, and production operations.