AI Platform DevOps & Observability Engineer

EY; Minneapolis (MN); Hybrid; USD 126,000 - 230,000; Full time

Job summary

EY is seeking an AI Systems Engineer to own the delivery, model-serving, routing, and observability of EY’s AI-native platform across cloud, on‑prem, edge, and air‑gapped environments.

You’ll build CI/CD pipelines, govern AI assets, control costs, and ensure end‑to‑end traceability with a focus on security, compliance, and transparent telemetry for every workload. This role bridges DevOps, MLOps, FinOps, and observability, partnering with platform and data teams.

Qualifications

• 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).
• 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).
• 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.

Responsibilities

• Own DevOps and delivery for AI workloads: build and operate CI/CD/CV pipelines that ship AI services, agents, and runtime components, including automated build, test, verification, release, and rollback.
• Own governance and discovery for AI assets, including service catalog/registry, experiment tracking and model metadata, upstream registries/mirrors, SBOM scanning, lineage contracts, and license management.
• Own resource and cost management, including quotas and rate limits, cost attribution and utilization, so AI execution stays economically bounded per tenant.
• Own the full observability stack, including metrics, logs, traces, dashboards, LLM debugging and evaluation, and SLA/alert notifications.
• Own the OpenTelemetry collection layer, including multi‑tenant receiver, exporters and queues, GPU telemetry, processor batching, and dynamic filtering.
• Automate GitOps-based delivery and continuous verification; embedding quality, integrity, and cost gates into pipelines so releases are policy‑compliant by default.
• Close the loop between delivery and observability by using telemetry, evaluation, and cost signals to drive deployment decisions, progressive rollout, and automated rollback.
• Ensure cost and telemetry are identity-stamped and per-tenant, so consumption and behavior are attributable end-to-end.

Skills

DevOps

MLOps

Observability

FinOps

OpenTelemetry

GPU Inference

Model Governance

LangSmith

API Gateway

Cost Attribution

Education

Bachelor’s or Master’s in CS

Tools

Ray Serve

vLLM

Triton

NIM

Prometheus

Grafana

Loki

Tempo

Jaeger

OpenTelemetry

Harbor

MLflow

Trivy

Kubecost

OpenCost

OpenLineage

Envoy

Kubernetes

Job description

EY is seeking an AI Systems Engineer to own the delivery, model-serving, routing, and observability of EY’s AI-native platform across cloud, on‑prem, edge, and air‑gapped environments.

You’ll build CI/CD pipelines, govern AI assets, control costs, and ensure end‑to‑end traceability with a focus on security, compliance, and transparent telemetry for every workload. This role bridges DevOps, MLOps, FinOps, and observability, partnering with platform and data teams.