Own the operational substrate of an AI-native retail decisioning platform.
Build platform-specific CI / CD pipelines on the enterprise spine.
Operate rollback orchestration with sub-15-minute recovery.
Own the platform observability stack — OpenTelemetry, Langfuse for LLM traces.
Implement the per-agent cost meter end-to-end and surface cost data to the enterprise GenAI cost dashboard.
Stand up the platform on-call rotation; author runbooks for every production agent and service.
Implement platform cost-tagging policy consistent with the enterprise standard.
Drive cost optimisation — right-sizing, caching, model routing decisions, reserved compute.

Bachelor's or Master's degree in Computer Science, Engineering, or a related discipline.
5+ years SRE / DevOps with production ownership.
Terraform at scale — modules, state, drift, environment promotion.
CI / CD for data + ML / AI services (GitLab CI / CD or comparable).
Cloud platform (Azure preferred; AWS / GCP transferable).
Observability — OpenTelemetry, Langfuse (or comparable LLM traces), custom dashboards.
FinOps — tagging policies, attribution, optimisation.
Incident response — on-call, post-mortems, runbook authorship.
AI / agent platform SRE experience; cost-meter / chargeback systems built or operated.
Multi-cloud production experience; open-source contributions to IaC / observability tooling.
AI / ML / agent system observability instrumentation (LLM cost, agent cost, eval scores).
Vendor certifications such as HashiCorp Terraform Associate / Professional, Azure Solutions Architect Associate, or Databricks Data Engineer Professional.

DevOps / SRE Engineer – AI Platform

Key skills