AI Platform Engineer & DevOps Observability Lead

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

Benefits offered by this job

Hybrid work model

Medical and dental coverage

Paid time off

Job summary

EY seeks an AI Systems Engineer to own delivery, model-serving, routing, and observability for an AI-native platform across cloud, on-prem, edge, and air-gapped environments. You will manage CI/CD/CV pipelines, governance, and cost visibility while ensuring secure, repeatable AI workloads.

This role sits at the intersection of DevOps, MLOps, FinOps and observability, requiring deep GPU inference experience and strong cost-management skills for multi-tenant environments.

Qualifications

• Bachelor’s or Master’s degree in Computer Science or related technical field.
• 8+ years in DevOps, MLOps, platform, or observability engineering, with hands-on production ownership of AI or high-throughput services.
• Hands-on DevOps experience, including CI/CD/CV pipelines and GitOps tooling for automated build, test, release, and rollback.
• Hands-on expertise operating inference/model-serving frameworks on GPU infrastructure.
• Strong observability experience with metrics, logs, traces, and OpenTelemetry.
• Experience with API gateways and request routing, including streaming responses.
• Experience with cost management / FinOps tooling and quota enforcement.
• Familiarity with model/artifact registries and governance tooling.
• Proven track record delivering AI or service infrastructure under compliance and security constraints.

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.
• Own resource and cost management to keep AI execution economically bounded per tenant.
• Own the full observability stack: metrics, logs, traces, dashboards, and debugging tools.
• Own the OpenTelemetry collection layer and multi-tenant telemetry routing.
• Automate GitOps-based delivery and verification with policy gates.
• Close the loop between delivery and observability using telemetry and cost signals to guide deployments.

Skills

DevOps

MLOps

Observability

Cost governance

Communication

Education

Bachelor’s or Master’s in Computer Science

Tools

ArgoCD

Helm

GitHub Actions

GitLab CI

Ray Serve

vLLM/Triton/NIM

Prometheus

Grafana

OpenTelemetry

Job description

EY seeks an AI Systems Engineer to own delivery, model-serving, routing, and observability for an AI-native platform across cloud, on-prem, edge, and air-gapped environments. You will manage CI/CD/CV pipelines, governance, and cost visibility while ensuring secure, repeatable AI workloads.

This role sits at the intersection of DevOps, MLOps, FinOps and observability, requiring deep GPU inference experience and strong cost-management skills for multi-tenant environments.