Remote-Eligible AI Systems Engineer & DevOps Observability

EY; Kansas City (MO); Hybrid; USD 126,000 - 230,000; Full time

Benefits offered by this job

Hybrid model

Medical and dental coverage

Pension and 401(k)

Paid time off

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 across cloud, on‑prem, edge, and air‑gapped environments.

This role combines DevOps, MLOps and FinOps to build automated pipelines, secure model execution, and cost‑visible, auditable workloads. EY offers a hybrid model and exposure to a global, diverse team.

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 ownership of AI or high-throughput services.
• Strong hands-on DevOps experience, including CI/CD/CV pipelines and GitOps tooling (ArgoCD, Helm, GitHub Actions/GitLab CI, or equivalents).
• Hands-on expertise operating inference/model-serving frameworks (Ray Serve, vLLM, Triton, or NIM) on GPU infrastructure.
• Strong experience with observability stacks (Prometheus, Grafana, Loki, Tempo/Jaeger) and OpenTelemetry.
• Experience with API gateways and request routing (Envoy or equivalent), including streaming responses.
• Experience with cost management / FinOps tooling (OpenCost, Kubecost, or equivalent) and quota/rate‑limit enforcement.
• Familiarity with model/artifact registries and supply‑chain scanning (Harbor, MLflow, Trivy/SBOM).
• Proven track record operating AI or service infrastructure under compliance, security, or regulatory constraints.
• Ability to define clean ownership boundaries and consumption contracts with platform, trust, and data teams.

Responsibilities

• Own DevOps and delivery for AI workloads: build and operate CI/CD/CV pipelines that ship AI services, agents, and runtime components with automated build, test, verification, release, and rollback.
• Own governance and discovery for AI assets, including service catalog/registry, experiment tracking and model metadata, registries/mirrors, SBOM scanning, lineage contracts, and license management.
• Own resource and cost management, including quotas and rate limits, cost attribution and utilization.
• Own the full observability stack: metrics, logs, traces, dashboards, LLM debugging and evaluation tools, and SLA/alert notifications.
• Own the OpenTelemetry collection layer, including multi‑tenant receiver, exporters and queues, GPU telemetry, and dynamic filtering.
• Automate GitOps‑based delivery and continuous verification with policy gates embedded in pipelines.
• Close the loop between delivery and observability using telemetry and cost signals to guide deployment decisions and automated rollbacks.
• Ensure cost and telemetry are identity‑stamped and per‑tenant.

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 across cloud, on‑prem, edge, and air‑gapped environments.

This role combines DevOps, MLOps and FinOps to build automated pipelines, secure model execution, and cost‑visible, auditable workloads. EY offers a hybrid model and exposure to a global, diverse team.