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AI Systems Engineer - DevOps& Observability - Senior

EY · Tallahassee, FL · 2 days ago
Hybrid$107k–$177k/yrFull-time

About the role

We are seeking an AI Systems Engineer to own the delivery, model-serving, routing, and observability layer of EY’s AI-native platform. This role advises on how AI services and agents are built and deployed, how models execute, how requests are routed to them, how AI assets are catalogued and governed, how consumption is measured and bounded, and how the entire platform is observed across cloud, on-prem, edge, and air-gapped environments.

Responsibilities

  • Build and operate CI/CD/CV pipelines that ship AI services, agents, and runtime components, including automated build, test, continuous verification, release, and rollback, ensuring AI workloads are delivered repeatably and safely into every environment.
  • Own governance and discovery for AI assets, including service catalog/registry, experiment tracking and model metadata, upstream registries/mirrors, CVE/SBOM scanning, lineage contracts, and license management.
  • Manage resource and cost management, including quotas and rate limits, cost attribution and utilization, so AI execution stays economically bounded and controllable per tenant and engagement.
  • Own the full observability stack, including metrics, logs, traces, dashboards, LLM debugging and evaluation, and SLA/alert notifications.
  • 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.

Requirements

Strong DevOps expertise: CI/CD/CV pipeline design, GitOps, continuous verification, and progressive/automated release and rollback for production workloads.

Deep expertise operating model-serving and inference systems (Ray, vLLM/Triton/NIM) on GPUs at production scale.

Strong observability skills: metrics, logs, traces, and OpenTelemetry.

Familiarity with model/artifact governance, registries, CVE scanning, and license/lineage tracking.

Comfortable operating across cloud, on-prem, edge, and air-gapped environments with consistent runtime and telemetry semantics.

Strong communicator able to explain runtime, cost, and observability tradeoffs to engineers, architects, and leadership.

Attributes for Success

  • Hands-on production ownership of AI or high-throughput services.
  • 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.

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