AI Systems Engineer - DevOps& Observability Manager

EY is seeking an AI Systems Engineer to own delivery, model serving, routing, governance, cost management, and observability for its AI-native platform. The role builds and operates pipelines and runtime systems that make AI workloads repeatable, economically bounded, discoverable, and traceable across cloud, on-premises, edge, and air-gapped environments.

Responsibilities:

• Own 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
• Own the OpenTelemetry collection layer, including multi-tenant receiver, exporters and queues (Kafka sink), DCGM exporter for GPU telemetry, processor batching, and dynamic filtering, so every signal is captured and routed reliably
• 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

Requirements:

• 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
• Deep observability skills: metrics, logs, traces, and OpenTelemetry
• FinOps mindset: able to attribute, bound, and optimize AI consumption cost per tenant and workload
• 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
• Bachelor's or Master's degree in Computer Science or related technical field
• 8+ years in DevOps, MLOps, platform, or observability engineering, with hands-on production ownership of AI or high-throughput services
• Strong hands-on DevOps experience, including CI/CD/CV pipelines and GitOps tooling (ArgoCD, Helm, GitHub Actions/GitLab CI, or equivalents) for automated build, test, release, and rollback
• Hands-on expertise operating inference/model-serving frameworks (Ray Serve, vLLM, Triton, or NIM) on GPU infrastructure
• Strong experience with observability stacks (Prometheus, Grafana, Loki, Tempo/Jaeger) and OpenTelemetry
• Experience with API gateways and request routing (Envoy or equivalent), including streaming responses
• Experience with cost management / FinOps tooling (OpenCost, Kubecost, or equivalent) and quota/rate-limit enforcement
• Familiarity with model/artifact registries and supply-chain scanning (Harbor, MLflow, Trivy/SBOM)
• Proven track record operating AI or service infrastructure under compliance, security, or regulatory constraints
• Ability to define clean ownership boundaries and consumption contracts with platform, trust, and data teams
• Experience with LLM evaluation and debugging tooling (LangSmith, Langfuse) and prompt/response quality measurement
• Experience with sandboxed/secure execution (gVisor, Firecracker, or microVM isolation) for untrusted or multi-tenant workloads
• Familiarity with GPU telemetry (DCGM) and GPU utilization optimization
• Experience with lineage and governance contracts (OpenLineage) and AI license management
• Exposure to multi-tenant cost attribution and per-tenant SLA/alerting
• Exposure to regulated delivery environments (financial services, tax, healthcare, risk)