AI Systems Engineer & DevOps Observability Lead

Ernst & Young Oman; Tampa (FL); Hybrid; USD 126,000 - 230,000; Full time

Benefits offered by this job

Hybrid work model

Medical and dental coverage

Pension and 401(k) plans

Paid time off

Job summary

EY seeks an AI Systems Engineer to own delivery, model-serving, routing, and observability for the AI-native platform. You’ll manage CI/CD pipelines, secure model execution, and cost governance across cloud, on‑prem, edge, and air‑gapped environments.

You’ll work at the intersection of DevOps, MLOps, FinOps, and observability, ensuring repeatable, economical, and transparent AI workloads with strong governance and telemetry.

Qualifications

• Bachelor’s or Master’s degree in Computer Science or related field.
• 8+ years in DevOps, MLOps, platform, or observability engineering with production ownership of AI workloads.
• Hands-on experience with CI/CD/CV pipelines and GitOps tooling for automated build, test, release, and rollback.
• Hands-on expertise operating AI or service infrastructure under compliance and security constraints.
• Experience with model/artifact registries and cost/telemetry tooling.

Responsibilities

• Own DevOps and delivery for AI workloads: CI/CD/CV pipelines, automated build, test, verification, release, and rollback.
• Own governance and discovery for AI assets with registries and metadata tracking.
• Own resource and cost management with quotas and per-tenant attribution.
• Own the full observability stack: metrics, logs, traces, dashboards, and SLA/alerting.
• Automate GitOps-based delivery and verification with policy gates.

Skills

DevOps

CI/CD

GitOps

Observability

GPU inference

OpenTelemetry

Cost governance

Education

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

Tools

Ray Serve

vLLM/Triton/NIM

MLflow

Harbor

Prometheus

Grafana

Loki

Envoy

Job description

EY seeks an AI Systems Engineer to own delivery, model-serving, routing, and observability for the AI-native platform. You’ll manage CI/CD pipelines, secure model execution, and cost governance across cloud, on‑prem, edge, and air‑gapped environments.

You’ll work at the intersection of DevOps, MLOps, FinOps, and observability, ensuring repeatable, economical, and transparent AI workloads with strong governance and telemetry.