SDE II - AI Platform

📋 Description

• Build the AI platform: APIs, SDKs, and self-service workflows so AI/ML engineers deploy, version
• Own GPU infrastructure: Cluster design, provisioning, scheduling, isolation, and utilisation
• Run inference in production: vLLM, Triton, KServe, or Ray — continuous batching, autoscaling, model
• Optimize relentlessly: Drive tokens/sec, p99 latency, GPU utilization, and cost per token through
• Engineer GPU-aware Kubernetes: GPU Operator, device plugins, GPU-aware scheduling, MIG
• Debug the hard layer: GPU OOMs, driver and CUDA runtime mismatches, interconnect bottlenecks

🎯 Requirements

• 2-4 years building and operating production software and infrastructure, with senior ownership of
• Bachelor's or Master's in Computer Science, Engineering, or equivalent practical experience.
• Strong Python and/or Go — you write and review production services, not just scripts and manifests.
• Deep Kubernetes and Docker: scheduling, resource management, operators, networking, debugging under
• Strong Linux, networking, storage, and distributed systems fundamentals.
• Production AWS/Azure/GCP experience — Lambda, API Gateway, EC2, S3, RDS and equivalents — with

🎁 Benefits

• Meaningful Equity: Every "Safestar" is a shareholder. You aren’t just an employee; you are a
• Unlimited Leaves: We don’t believe in clock-watching. We offer unlimited leave because we trust you
• Comprehensive Benefits: We provide top-tier medical insurance and wellness benefits to ensure you
• Career Trajectory: We are growing aggressively. For high-performers, the path for advancement moves