AI Platform Delivery & Observability Lead

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

Benefits offered by this job

Hybrid work model

Competitive compensation and benefits

Flexible vacation policy

Job summary

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

The role sits at the intersection of DevOps, MLOps, FinOps, and observability, requiring strong collaboration with platform, trust, and data teams. EY offers a hybrid work model and competitive compensation.

Qualifications

• Bachelor’s or Master’s degree in Computer Science or related field.
• 8+ years in DevOps, MLOps, platform, or observability with production ownership of AI or high-throughput services.
• Hands-on DevOps experience with CI/CD pipelines and GitOps tooling.
• Hands-on experience operating inference/model-serving frameworks on GPU infrastructure.
• Strong observability stack knowledge (Prometheus, Grafana, Loki).
• Experience with cost management tooling and quota enforcement.

Responsibilities

• Own DevOps and delivery for AI workloads: CI/CD/CV pipelines, automated build/test/release/rollback.
• Own governance and discovery for AI assets and registries.
• Own resource and cost management with quota/rate limits.
• Own the full observability stack and telemetry collection.
• Automate GitOps-based delivery and ensure policy-compliant releases by default.
• Tie telemetry and cost signals to deployment decisions and rollback.

Skills

DevOps

MLOps

Observability

Cost optimization

Effective communication

Education

Bachelor’s degree in CS or related

Tools

Envoy

OpenTelemetry

Ray Serve / Triton / NIM

Job description

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

The role sits at the intersection of DevOps, MLOps, FinOps, and observability, requiring strong collaboration with platform, trust, and data teams. EY offers a hybrid work model and competitive compensation.