AI Systems Engineer: DevOps, Observability & AI Platform

EY; Chattanooga (TN); 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 seeks an AI Systems Engineer to own the delivery, model-serving, routing, and observability layer of EY’s AI‑native platform. You will shape how AI services deploy, monitor, and govern workloads across cloud, on‑prem, edge, and air‑gapped environments.

You will work at the intersection of DevOps, MLOps, FinOps, and observability, building pipelines, securing model execution, and delivering end‑to‑end traceability in a regulated context.

Qualifications

• 8+ years in DevOps, MLOps, platform, or observability engineering.
• Hands‑on experience with CI/CD and GitOps tooling for automated build, test, release, and rollback.
• Hands‑on expertise operating inference/model‑serving frameworks on GPU infrastructure.
• Strong experience with observability stacks and OpenTelemetry.
• Familiarity with model/artifact registries and supply‑chain scanning.

Responsibilities

• Support DevOps and delivery for AI workloads: build and operate CI/CD/CV pipelines that ship AI services and runtime components.
• Own governance and discovery for AI assets, including registries and license management.
• Own resource and cost management, including quotas and rate limits, to keep AI execution bounded.
• Own the full observability stack: metrics, logs, traces, dashboards, and SLA/alerts.
• Automate GitOps-based delivery and continuous verification with policy gates.

Skills

DevOps

GitOps

CI/CD

OpenTelemetry

FinOps

AI governance

Education

Bachelor's or Master’s in Computer Science

Tools

Ray Serve

vLLM

Triton

NIM

Job description

EY seeks an AI Systems Engineer to own the delivery, model-serving, routing, and observability layer of EY’s AI‑native platform. You will shape how AI services deploy, monitor, and govern workloads across cloud, on‑prem, edge, and air‑gapped environments.

You will work at the intersection of DevOps, MLOps, FinOps, and observability, building pipelines, securing model execution, and delivering end‑to‑end traceability in a regulated context.