🛠 Tech Stack

💼 About This Role

You'll design and implement robust, scalable cloud infrastructure for deploying video AI models and services as part of our Video Center of Excellence. Your work will directly enable ML engineers to rapidly iterate and deploy state-of-the-art video understanding models at massive scale. This role offers the opportunity to shape how modern video AI infrastructure is built and deployed.

🎯 What You'll Do

• Design and implement cloud infrastructure for video AI models
• Build and maintain Kubernetes clusters optimized for GPU workloads
• Establish monitoring, logging, and observability for AI services
• Develop infrastructure-as-code practices for rapid deployment

📋 Requirements

• 4+ years of infrastructure or DevOps experience
• Deep hands-on expertise with Kubernetes and container orchestration
• Expert proficiency in at least one major cloud platform (AWS, GCP, Azure)
• Strong programming background in Python or Go

✨ Nice to Have

• Experience with GPU infrastructure or CUDA
• Hands-on experience with ML infrastructure tools (Kubeflow, MLflow, Ray)
• Experience with observability tools (Prometheus, ELK, Datadog)

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Phone Screen· 30 min
2. 2Technical Interview· 60 min
3. 3Hiring Manager Interview· 45 min