AI Platform Engineer: DevOps, Observability & Governance

EY; Seattle (WA); On-site; USD 107,000 - 177,000; Full time

Benefits offered by this job

Hybrid work model (implied)

Medical & dental coverage

401(k)

Job summary

EY is seeking an AI Systems Engineer to own the delivery, model-serving, routing, and observability layer of EY’s AI-native platform across cloud, on-prem, edge, and air-gapped environments. You’ll help design governance, cost tracking, and telemetry for repeatable AI workloads.

This role sits at the intersection of DevOps, MLOps, FinOps, and observability, requiring hands-on delivery pipelines, GPU inference, and deep observability with responsibility for AI services from development to

Qualifications

• 8+ years in DevOps, MLOps, platform, or observability engineering with hands-on production ownership of AI or high-throughput services.
• Hands-on CI/CD/CV pipelines and GitOps tooling (ArgoCD, Helm, GitHub Actions/GitLab CI) 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.

Responsibilities

• Own governance and discovery for AI assets, including service catalog/registry (Artifactory/Nexus, Harbor), experiment tracking and model metadata (MLflow).
• Own resource and cost management, including quotas and rate limits, cost attribution and utilization (Apptio/OpenCost/Kubecost).
• Own the full observability stack, including metrics (Prometheus/Mimir), logs (Loki), traces (Tempo/Jaeger), dashboards (Grafana).
• Own the OpenTelemetry collection layer, including multi-tenant receiver, exporters and queues (Kafka sink).
• Automate GitOps-based delivery and continuous verification; embedding quality, integrity, and cost gates into pipelines.
• Close the loop between delivery and observability by using telemetry, evaluation, and cost signals to drive deployment decisions.

Skills

DevOps

MLOps

Observability

Cost optimization

GPU inference

OpenTelemetry

Education

Bachelor's or Master’s degree in CS/related

Tools

Ray Serve

vLLM

Triton

Prometheus

Grafana

LangSmith

Langfuse

Envoy

Job description

EY is seeking an AI Systems Engineer to own the delivery, model-serving, routing, and observability layer of EY’s AI-native platform across cloud, on-prem, edge, and air-gapped environments. You’ll help design governance, cost tracking, and telemetry for repeatable AI workloads.

This role sits at the intersection of DevOps, MLOps, FinOps, and observability, requiring hands-on delivery pipelines, GPU inference, and deep observability with responsibility for AI services from development to