AI Systems Engineer - DevOps& Observability - Senior

Key Responsibilities

• Supports DevOps and delivery for AI workloads: build and operate the CI/CD/CV pipelines that ship AI services, agents, and runtime components, including automated build, test, continuous verification, release, and rollback, so AI workloads are delivered repeatably and safely into every environment.
• Own governance and discovery for AI assets, including service catalog/registry (Artifactory/Nexus, Harbor), experiment tracking and model metadata (MLflow), upstream registries/mirrors (HuggingFace/NGC), CVE/SBOM scanning (Trivy), lineage contracts (OpenLineage), and license management.
• Own resource and cost management, including quotas and rate limits, cost attribution and utilization (Apptio/OpenCost/Kubecost), so AI execution stays economically bounded and controllable per tenant and engagement.
• Own the full observability stack, including metrics (Prometheus/Mimir), logs (Loki), traces (Tempo/Jaeger), dashboards (Grafana), LLM debugging and evaluation (LangSmith/Langfuse), and SLA/alert notifications.
• Automate GitOps-based delivery and continuous verification; embedding quality, integrity, and cost gates into pipelines so releases are policy-compliant by default rather than by manual review.
• Close the loop between delivery and observability by using telemetry, evaluation, and cost signals to drive deployment decisions, progressive rollout, and automated rollback of AI workloads.
• Ensure cost and telemetry are identity-stamped and per-tenant, so consumption and behavior are attributable end-to-end, keeping FinOps and observability tied to the workloads that generate the load.

Skills And Attributes For Success

• Strong DevOps expertise: CI/CD/CV pipeline design, GitOps, continuous verification, and progressive/automated release and rollback for production workloads.
• Deep expertise operating model-serving and inference systems (Ray, vLLM/Triton/NIM) on GPUs at production scale.
• Strong observability skills: metrics, logs, traces, and OpenTelemetry.
• Familiarity with model/artifact governance, registries, CVE scanning, and license/lineage tracking.
• Comfortable operating across cloud, on-prem, edge, and air-gapped environments with consistent runtime and telemetry semantics.
• Strong communicator able to explain runtime, cost, and observability tradeoffs to engineers, architects, and leadership.