Senior AI Platform & Observability Engineer

EY; Rogers (AR); Hybrid; USD 107,000 - 177,000; Full time

Benefits offered by this job

Medical and dental coverage

Pension and 401(k)

Paid time off

Job summary

EY is seeking an AI Systems Engineer to own delivery, model serving, routing, and observability across EY’s AI-native platform. You will shape how AI services deploy, how models execute, and how costs and telemetry are bounded and observed in cloud, on-prem, edge, and air-gapped environments.

The role sits at the intersection of DevOps, MLOps, FinOps, and observability, requiring hands-on work with pipelines, inference systems on GPUs, and robust governance and cost controls.

Qualifications

• 8+ years in DevOps, MLOps, platform, or observability engineering with hands-on ownership of AI or high-throughput services.
• Hands-on DevOps experience with CI/CD/CV pipelines and GitOps tooling (ArgoCD, Helm, GitHub Actions/GitLab CI).
• Experience operating inference/model-serving frameworks (Ray Serve, vLLM, Triton, NIM) on GPU infrastructure.
• Strong observability experience with Prometheus, Grafana, Loki, Tempo/Jaeger.
• Familiarity with model/artifact registries and supply-chain scanning (Harbor, MLflow, Trivy/SBOM).
• Experience with cost management/FinOps tooling (OpenCost, Kubecost) and quota/rate-limit enforcement.
• Experience with API gateways and request routing (Envoy) including streaming.
• Ability to define ownership boundaries and contracts with platform, trust, and data teams.

Responsibilities

• Supports DevOps and delivery for AI workloads: CI/CD/CV pipelines, GitOps, automated build/test/verification, release and rollback.
• Own governance and discovery for AI assets: catalog/registry, metadata, upstream registries, scanning, lineage, licenses.
• Own resource and cost management: quotas, rate limits, cost attribution, per-tenant budgeting.
• Own the full observability stack: metrics, logs, traces, dashboards, debugging tools, SLA/alerts.
• Automate GitOps-based delivery and verification with policy gates.
• Close the loop between delivery and observability using telemetry and cost signals for rollout decisions.
• Ensure identity-stamped telemetry per-tenant for FinOps and observability.

Skills

DevOps

MLOps

observability

FinOps

model governance

multi-environment

LLM evaluation

prompt quality

Education

Bachelor’s or Master’s degree in Computer Science or related field

Tools

Ray Serve

vLLM

Triton

NIM

Prometheus

Grafana

Loki

Tempo/Jaeger

Envoy

Harbor

MLflow

Trivy

OpenLineage

OpenCost

Kubecost

Job description

EY is seeking an AI Systems Engineer to own delivery, model serving, routing, and observability across EY’s AI-native platform. You will shape how AI services deploy, how models execute, and how costs and telemetry are bounded and observed in cloud, on-prem, edge, and air-gapped environments.

The role sits at the intersection of DevOps, MLOps, FinOps, and observability, requiring hands-on work with pipelines, inference systems on GPUs, and robust governance and cost controls.