Senior MLOps Engineer

Quick Facts

• Role: Senior MLOps Engineer
• Work mode: Fully remote (Central/Eastern Europe)

Description

As a Senior MLOps Engineer, you will architect, build, and maintain the infrastructure, pipelines, and tooling that enable AI models to be deployed, scaled, and monitored in production on Google Cloud Platform (GCP). You will collaborate closely with AI Researchers, Data Engineers, and Backend teams to bridge experimentation with 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 model deployment lifecycle and build high-throughput, low-latency inference services
• Build automated, reproducible CI/CD/CT pipelines for training, testing, evaluation, and deployment
• Implement production observability for system health and ML-specific metrics (e.g., feature drift, prediction accuracy, data distribution shifts)
• Provide scalable training environments and standardized deployment templates for AI research engineers
• Integrate model pipelines with feature stores, dataset versioning, and stream/batch processing workflows
• Lead the technical transition of prototypes/notebooks into resilient, secure, auto-scaling microservices

Requirements

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

Benefits

• Help operationalize AI by making production AI reliable at scale
• Provide the infrastructure backbone that empowers AI teams to innovate quickly while maintaining system stability
• Work as a cross-functional bridge between data science, cloud operations, and software engineering