AI Systems Engineer

Location: Anywhere in Country

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The opportunity

We are seeking an AI Systems Engineer to own the delivery, model-serving, routing, and observability layer of EY’s AI-native platform. These are the systems that ship, run, and make fully visible every AI workload. Within the Hybrid AI Multi-Environment Runtime (HAI), this role owns 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.

This is a distinct discipline from platform, data, and trust engineering. Where Platform Engineering owns the cluster substrate and its infrastructure automation, this role owns the delivery and runtime surface, including the CI/CD/CV pipelines that ship AI workloads, secure model execution, semantic routing, and model/prompt selection, together with the governance, discovery, cost, and telemetry systems that keep AI workloads shippable, economical, discoverable, and transparent. It sits at the intersection of DevOps, MLOps, FinOps, and observability.

This role is ideal for an engineer who is equally comfortable building automated delivery pipelines, operating high-performance inference (GPUs, model servers, sandboxed execution), and building deep observability and cost visibility; who understands that in regulated contexts every AI workload must be delivered repeatably and every AI request must be economically bounded, attributable, and traceable end-to-end.

Your key responsibilities

• Own 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.