AI Systems Engineer: DevOps, Platform & Observability

EY; McLean (VA); Hybrid; USD 107,000 - 177,000; Full time

Benefits offered by this job

Hybrid work model

Medical and dental coverage

Pension and 401(k)

Job summary

EY is seeking an AI Systems Engineer to own delivery, model-serving, routing, and observability for EY’s AI-native platform across cloud, on‑prem, edge, and air‑gapped environments. You will shape the runtime surface, CI/CD pipelines, and governance to ensure repeatable, auditable AI workloads with cost-aware execution.

You’ll work across DevOps, MLOps, FinOps, and observability, coordinating with platform, trust, and data teams to deliver secure, scalable AI infrastructure in a hybrid model.

Qualifications

• 8+ years in DevOps, MLOps, platform, or observability engineering with hands-on production 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) for automated build, test, release, and rollback.
• 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

• Supports DevOps and delivery for AI workloads: build and operate the CI/CD/CV pipelines that ship AI services, agents, and runtime components, including automated build, test, continuous verification, release, and rollback, so AI workloads are delivered repeatably and safely into every environment.
• Own governance and discovery for AI assets, including service catalog/registry (Artifactory/Nexus, Harbor), experiment tracking and model metadata (MLflow), upstream registries/mirrors (HuggingFace/NGC), CVE/SBOM scanning (Trivy), lineage contracts (OpenLineage), and license management.
• Own resource and cost management, including quotas and rate limits, cost attribution and utilization (Apptio/OpenCost/Kubecost), so AI execution stays economically bounded and controllable per tenant and engagement.
• Own the full observability stack, including metrics (Prometheus/Mimir), logs (Loki), traces (Tempo/Jaeger), dashboards (Grafana), LLM debugging and evaluation (LangSmith/Langfuse), and SLA/alert notifications.
• Own the OpenTelemetry collection layer, including multi‑tenant receiver, exporters and queues (Kafka sink), DCGM exporter for GPU telemetry, processor batching, and dynamic filtering, so every signal is captured and routed reliably.
• Automate GitOps-based delivery and continuous verification; embedding quality, integrity, and cost gates into pipelines so releases are policy‑compliant by default rather than by manual review.
• Close the loop between delivery and observability by using telemetry, evaluation, and cost signals to drive deployment decisions, progressive rollout, and automated rollback of AI workloads.
• Ensure cost and telemetry are identity‑stamped and per‑tenant, so consumption and behavior are attributable end‑to‑end, keeping FinOps and observability tied to the workloads that generate the load.

Skills

DevOps

MLOps

Observability

FinOps

Cost attribution

Artifact governance

Cloud + on-prem + edge

Communication

Education

Bachelor’s degree in Computer Science or related field

Tools

Ray Serve

vLLM

Triton

NIM

ArgoCD

Helm

GitHub Actions

GitLab CI

Prometheus

Grafana

Loki

Tempo

Jaeger

OpenTelemetry

Kubecost

OpenCost

Harbor

MLflow

Trivy

Job description

EY is seeking an AI Systems Engineer to own delivery, model-serving, routing, and observability for EY’s AI-native platform across cloud, on‑prem, edge, and air‑gapped environments. You will shape the runtime surface, CI/CD pipelines, and governance to ensure repeatable, auditable AI workloads with cost-aware execution.

You’ll work across DevOps, MLOps, FinOps, and observability, coordinating with platform, trust, and data teams to deliver secure, scalable AI infrastructure in a hybrid model.