🛠 Tech Stack

💼 About This Role

You'll architect and lead the infrastructure strategy for a next-generation Production ML platform on Google Cloud. You will build the "paved road" for engineers, automating everything from model deployment to complex networking perimeters. This role impacts high-performance machine learning workloads in a high-trust, outcome-focused team.

🎯 What You'll Do

• Design and deploy elastic scaling cloud infrastructure (GCP) and Kubernetes for ML workloads
• Build automated CI/CD pipelines for training, testing, and deploying ML models
• Implement observability tools to track model drift, accuracy, latency, and performance
• Collaborate with data, ML, backend, and frontend engineers for smooth production operations

📋 Requirements

• 8-10+ years in DevOps/Platform Engineering with 2+ years in production ML workloads
• Deep hands-on experience with GCP (VPC-SC, IAM) and GKE (Helm, Kustomize, ArgoCD)
• High proficiency with Istio (VirtualServices, mTLS) and API Gateways (Kong)
• Expert-level Terraform skills with Atlantis/GitOps workflow across large estates

✨ Nice to Have

• Experience with ML Observability (monitoring model accuracy, drift detection)
• Certifications like CKA/CKS or GCP Professional Cloud Architect
• Familiarity with Loki, Grafana, or ClickHouse at scale

🎁 Benefits & Perks

• 🏖️ Remote-first work from anywhere in Estonia
• 💰 Competitive compensation (inferred from senior role)
• 🚀 Accelerated career growth in fast-paced environment

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Screen· 30 min
2. 2Technical Interview· 60 min
3. 3On-site (virtual) round· 3-4 hours