Senior Site Reliability Engineer (AI/ML Platform)

We are looking for a seasoned Site Reliability Engineer to join the team responsible for the backbone of global AI/ML services. This is an opportunity to safeguard a massive, distributed AI compute platform, ensuring AI models and GPU-powered infrastructure are reliable, observable, and built to last.

If you are passionate about building and operating large-scale systems and are excited by the unique challenges of the AI/ML world, this is the role for you.

What You Will Do (Your Impact)

• Build Bulletproof Observability: Design and implement the “nervous system” for the AI platform. Go beyond basic monitoring to build comprehensive observability with robust telemetry, insightful Grafana dashboards, and intelligent Prometheus alerting. Define and track SLOs/SLIs to ensure services meet their promises and drive improvements when they do not.
• Automate Everything: Write clean, effective code in Python or Go to eliminate manual toil, create self-healing systems, and build sophisticated tooling that accelerates incident response and makes deployments safer.
• Own the Incident Response Lifecycle: Lead incident management when critical systems fail, participate in a blameless on-call rotation, and conduct insightful post-mortems that result in lasting improvements. Build the runbooks others will rely on.
• Engineer a World-Class Deployment Pipeline: Contribute to the CI/CD ecosystem by building robust integrations, automated safety checks, and seamless rollback capabilities, enabling innovation at speed without sacrificing stability.
• Act as a Reliability Partner for Product Teams: Work alongside product engineers building the next generation of AI services. Advise on reliability, help shape architecture, and ensure products are operationally sound before production.

Who We’re Looking For (Your Profile)

• A Site Reliability, Platform, or Infrastructure Engineer with a proven track record of managing complex, large-scale distributed systems.
• Deep, practical experience managing large-scale Kubernetes containerized environments and understanding orchestration under heavy load.
• Hands-on experience with observability tools such as Prometheus, Grafana, and distributed tracing systems.
• Strong programming ability, including clean, scalable automation scripts and infrastructure-as-code using Python or Go and Terraform.
• Curiosity about, or direct experience with, AI/ML infrastructure challenges such as model-serving pipelines, inference engines, or GPU-accelerated workloads.
• A problem-solver who takes ownership of issues from start to finish and focuses on preventing recurrence.
• Strong collaboration and mentoring skills, with the ability to help engineers adopt SRE principles and build more reliable software.