DevOps - Platform / Reliability Engineer

Requirements:

• 6-10 years of experience in DevOps, platform engineering, site reliability engineering, infrastructure engineering, or a closely related engineering role.
• Strong understanding of how production systems fail and the ability to identify failure modes, blast radius, and single points of failure before they bite.
• Hands-on experience building or owning infrastructure and delivery platforms end-to-end, not just operating someone else's.
• Strong hands-on experience with containers and orchestration in production and with infrastructure as code.
• Strong Linux and networking fundamentals: DNS, TLS, load balancing, routing, and firewalls, enough to debug from first principles.
• Able to read, review, and direct code in Python, Go, or a comparable language. AI writes most of the code here; your job is to judge whether it is correct, safe, and something we can run in production.
• Practical experience with observability: metrics, logging, tracing, and instrumentation.
• Experience carrying production on-call, leading incidents, and writing postmortems that changed something.
• Working knowledge of infrastructure security: secrets management, identity and access design, and hardening practices.
• Deeply hands-on with AI engineering tools such as Claude Code, Grok, and Codex, used for infrastructure code, debugging, log and incident analysis, and automation.
• Strong debugging skills and the ability to trace an issue across every layer of a live system, from application code to the network.
• Ability to work across multiple clouds, providers, frameworks, and tools based on what the product requires.
• Ability to balance speed, reliability, security, cost, and maintainability in a fast-moving environment.

Added advantage:

• Experience operating GPU infrastructure: fleet provisioning and health, GPU scheduling, driver and firmware lifecycle, or multi-tenant GPU isolation. Not required today, but valuable as we bring our own capacity in-house.
• Experience keeping model-serving or inference endpoints reliable at scale: autoscaling, routing, rollout, and protecting p99 latency.
• Capacity planning, scale testing, and disaster recovery exercises run as a discipline rather than an annual event.
• Experience in a managed-services or SaaS operations environment, with incident, problem, and change management run to an agreed standard.