Senior SRE Engineer (MLOps) - AI

Description

Key Responsibilities

• Own reliability for ML and agentic AI services in production — SLOs, dashboards, alerts, runbooks, and incident follow-ups
• Build observability across the AI stack — latency, errors, traces, tool calls, cost, and user impact
• Design safe-release patterns for models, prompts, agents, tools, and configuration, including canary, rollback, feature-flag, and evaluation-gate strategies
• Provide operational support for inference APIs, queues, retrieval layers, and AI workflows running on Kubernetes/EKS
• Establish ownership, traceability, and guardrails around what agentic systems (e.g. Sidekick, the growth advisor) are allowed to do, including how they call internal tools
• Defend agent tool-calling against prompt injection and untrusted-data risks — establish and enforce data-trust boundaries so that untrusted store/merchant content cannot manipulate agent decisions, tool calls, or actions
• Drive AI cost governance — per-model and per-pod spend visibility, token-cost tracking, and anomaly alerting
• Build automation and self-service paths so product teams have a known safe path to production instead of rebuilding it each time
• Turn recurring operational pain into simple, reusable platform standards that other teams adopt
• Participate in architecture discussions, code reviews, and technical decision-making

Requirements

• 4+ years in SRE, platform engineering, DevOps, or production infrastructure, operating distributed systems in production — not only in demos
• Hands-on experience with Kubernetes and cloud-native systems in production
• Familiarity with deploying ML projects
• Strong command of CI/CD, GitOps, observability, and incident response
• Solid experience with infrastructure-as-code, secrets management, and networking
• Ability to write automation or platform tooling in Python, or a similar language
• Production judgment — knowing how to make systems measurable, debuggable, repeatable, and safe to change (you do not need to be a machine learning researcher)
• Ability to work across teams, explain trade-offs clearly, and turn operational pain into standards engineers will actually use
• Nice to have:
• Experience with MLOps or ML platforms — model serving, registries, evaluation, feature/data dependencies, drift monitoring, or ML pipelines
• Familiarity with LLM applications or agentic systems — RAG, vector databases, tool calling, workflow orchestration, memory, traces, guardrails, or evaluation pipelines
• Exposure to tooling such as OpenTelemetry, Prometheus, Grafana, MLflow, KServe, Ray, LiteLLM, vLLM, LangGraph, Arize Phoenix, or LangSmith
• Experience with Kafka consumers, GPU workloads, inference optimization, model routing, or AI cost governance
• Experience working in cross-functional product teams involving AI, backend, and frontend engineers