Senior MLOps Engineer
Core
Architecting, building, and maintaining GCP-based infrastructure, pipelines, and tooling to deploy, scale, and monitor complex AI models in production.
Role type
Senior MLOps Engineer
Builds
Scalable ML infrastructure, high-throughput inference services, automated CI/CD/CT pipelines, and production observability systems.
Domain
Cybersecurity / Cloud Infrastructure / Machine Learning Operations
Deliverable
production ML models
Required skills
GCP ecosystem mastery (Vertex AI, GKE, Cloud Run, IAM/VPC), containerization (Docker, Kubernetes), model serving frameworks (Triton, vLLM, MLflow), workflow orchestration (Airflow, Vertex AI Pipelines), Infrastructure as Code (Terraform), Python, SQL, ML observability (drift detection, telemetry).
Preferred skills
Large-scale LLM/Deep Learning inference/training experience, GCP Professional certifications, feature store familiarity (Feast, Vertex AI Feature Store).
Technologies
Google Cloud Platform, Vertex AI, GKE, Cloud Run, GCS, Triton Inference Server, vLLM, MLflow, Airflow, GitHub Actions, ArgoCD, Terraform, Docker, Grafana, Prometheus.
Responsibilities
Architect and manage scalable GCP ML infrastructure; own end-to-end model deployment lifecycle; build automated training and deployment pipelines; implement system and ML-specific monitoring; support AI engineers with scalable training environments; transition AI prototypes to production microservices.
Seniority
Senior, hands-on IC