🛠 Tech Stack

💼 About This Role

You'll architect and lead the infrastructure strategy for our next-generation Production ML platform on Google Cloud. Your core impact will be building the "paved road" for engineers, automating model deployment and networking perimeters. Join a high-trust, outcome-focused team solving challenging ML problems.

🎯 What You'll Do

• Design and maintain elastic scaling GCP infrastructure and Kubernetes for ML workloads
• Build CI/CD pipelines for training, testing, and deploying ML models
• Implement observability tools for model drift, accuracy, and performance
• Collaborate across data, ML, backend, and frontend engineering teams

📋 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)
• Expert-level Terraform with Atlantis/GitOps workflow across large codebase
• Experience operating Airflow in production and ML-serving stack (Triton, MLflow)

✨ Nice to Have

• Experience with continuous monitoring of model accuracy and data drift
• Ansible for cluster bootstrap and recovery
• Kubernetes (CKA/CKS) or GCP certifications

🎁 Benefits & Perks

• 🔬 Solve real customer problems with impactful work
• 📈 See your impact in a scrappy, nimble organization
• 🚀 Accelerate your career with new technologies and growth
• 🤝 Work with talented people in a growth-oriented environment

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter call· 30 min
2. 2Technical interview· 60 min
3. 3System design interview· 60 min
4. 4Hiring manager interview· 45 min
5. 5Offer· 30 min