AI Platform Engineer: DevOps, Observability & FinOps

EY; Los Angeles (CA); Remote; USD 107,000 - 177,000; Full time

Job summary

EY seeks an AI Systems Engineer to own delivery, model-serving, routing, and observability for EY’s AI-native platform. You will shape how AI services are built, deployed, and governed across cloud, on‑prem, edge, and air‑gapped environments, collaborating with senior engineers to test and enhance capabilities.

This role sits at the intersection of DevOps, MLOps, FinOps, and observability, requiring hands-on work with GPUs, model servers, and secure execution, while ensuring regulatory

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 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 observability experience with Prometheus, Grafana, Loki, Tempo/Jaeger and OpenTelemetry.
• Experience with API gateways and request routing (Envoy or equivalent), including streaming responses.
• Familiarity with cost management/FinOps tooling and quota/rate-limit enforcement.
• Knowledge of model/artifact registries and supply-chain scanning (Harbor, MLflow, Trivy/SBOM).
• Proven track record operating AI or service infrastructure under regulatory constraints and defining ownership boundaries.

Responsibilities

• Build and operate CI/CD/CV pipelines that ship AI services, agents, and runtime components.
• Own governance and discovery for AI assets, including registries, model metadata, and license management.
• Manage resource and cost control, including quotas and attribution for tenants and workloads.
• Oversee the full observability stack: metrics, logs, traces, dashboards, and AI evaluation tooling.
• Automate delivery with GitOps and embed quality and cost gates into pipelines.
• Drive deployment decisions with telemetry and cost signals, enabling safe rollouts and automated rollbacks.
• Ensure end-to-end identity stamping for consumption and performance across environments.

Skills

DevOps expertise

MLOps experience

Observability

FinOps mindset

Regulatory compliance

Education

Bachelor's or Master’s in CS

Tools

ArgoCD

Helm

GitHub Actions

GitLab CI

Ray Serve

vLLM

Triton

NIM

Prometheus

Grafana

Loki

Tempo

Jaeger

OpenTelemetry

Envoy

OpenCost

Kubecost

Harbor

MLflow

Trivy

Job description

EY seeks an AI Systems Engineer to own delivery, model-serving, routing, and observability for EY’s AI-native platform. You will shape how AI services are built, deployed, and governed across cloud, on‑prem, edge, and air‑gapped environments, collaborating with senior engineers to test and enhance capabilities.

This role sits at the intersection of DevOps, MLOps, FinOps, and observability, requiring hands-on work with GPUs, model servers, and secure execution, while ensuring regulatory