Platform Engineer - AI Infrastructure

Highlights

• Build the platform atop a multi-vendor GPU fleet for training and inference.
• Turn model artifacts into scalable multi-tenant endpoints with canary rollouts.
• Create self-service CLI and APIs that let ML teams use thousands of GPUs.

Ideal candidate

A builder-minded systems engineer who treats internal platforms as products and loves enabling ML teams at scale. You take end-to-end ownership and thrive in fast-moving, high-bar teams tackling population-scale AI for India.

Requirements

• Strong systems engineering background with deep Kubernetes controller and internals fluency
• Comfortable building in Go or Python
• Working literacy in GPU platform constraints like MIG, GPU sharing, gang scheduling and topology-aware placement
• Understand scheduler and API machinery including where abstractions leak
• Product mindset toward internal users, measuring platform by adoption and self-service
• Ability to own a capability end to end from design through rollout and documentation