Remote AI Systems Engineer & DevOps Observability Lead

EY; Chattanooga (TN); Hybrid; USD 126,000 - 230,000; Full time

Benefits offered by this job

Hybrid work model

Medical and dental coverage

Pension and 401(k)

Job summary

EY seeks an AI Systems Engineer to own delivery, model-serving, routing, and observability for EY’s AI-native platform. You’ll manage CI/CD/CV pipelines, governance of AI assets, cost attribution, and end-to-end observability across cloud, on-prem, edge, and air-gapped environments.

You will operate across DevOps, MLOps, FinOps, and observability, working with GPUs and modern inference frameworks to deliver repeatable, economical AI workloads with strong guardrails.

Qualifications

• Bachelor’s or Master’s in Computer Science or related technical field.
• 8+ years in DevOps, MLOps, platform, or observability engineering with hands-on AI or high-throughput services.
• Strong hands-on DevOps experience with CI/CD/CV pipelines and GitOps tooling (ArgoCD, Helm, GitHub Actions/GitLab CI).

Responsibilities

• Own DevOps and delivery for AI workloads: CI/CD/CV pipelines for AI services and runtime components.
• Own governance and discovery for AI assets and model metadata.
• Own resource and cost management, including quotas and cost attribution.
• Own the full observability stack: metrics, logs, traces, dashboards, and telemetry.
• Own the OpenTelemetry collection layer across multi-tenant environments.
• Automate GitOps-based delivery and continuous verification with policy gates.
• Close the loop between delivery and observability using telemetry and cost signals.
• Ensure cost/telemetry are identity-stamped and per-tenant.

Skills

CI/CD

GitOps

OpenTelemetry

Kubernetes

Ray Serve

GPU Ops

Education

Bachelor’s or Master’s in CS or related field

Tools

ArgoCD

Helm

GitHub Actions

GitLab CI

OpenLineage

Kubecost

Prometheus

Grafana

Job description

EY seeks an AI Systems Engineer to own delivery, model-serving, routing, and observability for EY’s AI-native platform. You’ll manage CI/CD/CV pipelines, governance of AI assets, cost attribution, and end-to-end observability across cloud, on-prem, edge, and air-gapped environments.

You will operate across DevOps, MLOps, FinOps, and observability, working with GPUs and modern inference frameworks to deliver repeatable, economical AI workloads with strong guardrails.