Design, deploy, and maintain elastic scaling cloud infrastructure (GCP) and containerization tools like Kubernetes.
Build automated pipelines for training, testing, and deploying machine learning models using tools like Jenkins, GitHub Actions, or Airflow.
Implement observability tools to track model drift, accuracy, latency, and performance degradation in production.
Collaborate between data engineers, ML engineers, Backend and Frontend engineers.
Implement comprehensive monitoring for system health and ML-specific metrics for long-term model reliability.
Deploy tools that empower individual teams to monitor their workloads.
Participate in on-call rotation and manage compliance posture with standards such as SOC.

8
10+ years in DevOps/Platform Engineering, with at least 2 years of experience operating and maintaining production ML workloads.
Deep, hands-on experience with GCP (VPC-SC, IAM, Organization Policies) and GKE (Cluster topology, Helm, Kustomize).
High proficiency with Istio and API Gateways (Kong).
Expert-level Terraform skills using an Atlantis/GitOps workflow.
Experience managing enterprise-grade identity and secrets (Auth0, Dex, ESO, or SOPS).
Experience operating Airflow in production and an ML-serving stack (Triton, vLLM, MLflow).
Comfortable managing Cloud SQL (PostgreSQL), BigQuery, Elastricsearch, or ClickHouse.
At least an upper-intermediate level of spoken and written English.

Solve real customer problems.
See your impact.
Accelerate your career.
Opportunity to learn new technologies, products, and markets in a fast-paced, growth-oriented environment.
Work with other talented people at a company where people matter.

Principal Platform Engineer

Key skills