AI Systems Engineer: DevOps, Observability & FinOps

EY; Minneapolis (MN); Ibrido; USD 107.000 - 177.000; Tempo pieno

Vantaggi offerti da questo lavoro

Hybrid work model

Medical and dental coverage

Pension and 401(k) plans

Paid time off

Descrizione del lavoro

EY is seeking an AI Systems Engineer to own the delivery, model-serving, routing, and observability layers of EY’s AI-native platform. The role spans cloud, on-prem, edge, and air-gapped environments, collaborating with senior engineers to test and develop capabilities.

This role sits at the intersection of DevOps, MLOps, FinOps, and observability, requiring strong cross‑functional communication and a focus on repeatable, auditable AI workloads.

Competenze

• 8+ years in DevOps/MLOps/observability with ownership of AI workloads.
• CI/CD pipelines and GitOps tooling for automated build, test, release, and rollback.

Mansioni

• Supports DevOps for AI workloads and CI/CD pipelines across environments.
• Own governance and discovery for AI assets and model metadata.
• Manage resource cost attribution and tenant isolation.
• Own observability stack: metrics, logs, traces, dashboards.
• Own OpenTelemetry collection and multi-tenant data routing.
• Automate GitOps-based delivery and verification with policy gates.
• Drive deployment decisions using telemetry and cost signals.
• Ensure FinOps and observability are tied to workloads.

Conoscenze

DevOps/MLOps

CI/CD pipelines

GitOps tooling

OpenTelemetry

FinOps

Model serving

Observability

Regulated delivery

Communication

Formazione

Bachelor's or Master's in CS

Strumenti

Ray Serve

vLLM/Triton/NIM

Prometheus/Grafana/Loki

Envoy API gateway

Harbor/MLflow/Trivy

Descrizione del lavoro

EY is seeking an AI Systems Engineer to own the delivery, model-serving, routing, and observability layers of EY’s AI-native platform. The role spans cloud, on-prem, edge, and air-gapped environments, collaborating with senior engineers to test and develop capabilities.

This role sits at the intersection of DevOps, MLOps, FinOps, and observability, requiring strong cross‑functional communication and a focus on repeatable, auditable AI workloads.