Senior MLOps Engineer

Quick Facts

• Fully remote (multiple countries)
• Role: Senior MLOps Engineer on Google Cloud Platform (GCP)

Description

As a Senior MLOps Engineer, you will architect, build, and maintain the infrastructure, pipelines, and tooling that enable complex AI models to be deployed, scaled, and monitored in production on GCP. You will collaborate with AI researchers, data engineers, and backend teams to bridge experimentation and enterprise-grade production systems.

Responsibilities

• Architect and manage scalable GCP ML infrastructure using Vertex AI, GKE, GCS, Cloud Run, and GPU/TPU compute
• Own the end-to-end deployment lifecycle for ML models and build low-latency, high-throughput inference services (e.g., Triton, vLLM, MLflow)
• Implement CI/CD and training pipelines for reproducible training, testing, evaluation, and deployment (Airflow, Vertex AI Pipelines, GitHub Actions)
• Set up production observability for both system health and ML-specific metrics (feature drift, prediction accuracy, data distribution shifts) and support automated retraining triggers
• Provide scalable training environments and standardized deployment templates for AI research engineers
• Integrate model pipelines with feature stores, dataset versioning, and stream/batch data workflows
• Lead the transition of AI prototypes and notebooks into resilient, secure, auto-scaling microservices

Requirements

• At least 5 years of hands-on production MLOps experience designing, deploying, and maintaining ML workloads in cloud environments
• Deep practical experience with GCP including Vertex AI, Cloud Storage, GKE, Cloud Run, and IAM/VPC configuration
• Expertise in containerization and Kubernetes/GKE plus specialized serving tools (Triton, vLLM, MLflow)
• Proven experience with workflow orchestrators and CI/CD tools (Airflow, Vertex AI Pipelines, GitHub Actions, ArgoCD)
• Solid Infrastructure as Code experience with Terraform
• Proficiency in Python and SQL
• Hands-on ML observability experience (logging, telemetry, drift detection) using tools such as Grafana, Prometheus, GCP Cloud Monitoring, or specialized ML observability frameworks

Benefits

• Opportunity to operationalize AI with production reliability at scale
• Build an infrastructure backbone that enables rapid innovation while maintaining stability
• Cross-functional collaboration bridging data science, cloud operations, and software engineering