AI Systems Engineer - DevOps& Observability - Senior

EY•Hartford, DC

3d•$106,900 - $200,600•Hybrid

About The Position

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 advised 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. Works with senior engineers to test and develop capabilities. 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.

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.
Deep observability skills: metrics, logs, traces, and OpenTelemetry.
FinOps mindset: able to attribute, bound, and optimize AI consumption cost per tenant and workload.
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.
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.

Nice To Haves

Bachelor’s or Master’s degree in Computer Science or related technical field.
Experience with LLM evaluation and debugging tooling (LangSmith, Langfuse) and prompt/response quality measurement.
Experience with sandboxed/secure execution (gVisor, Firecracker, or microVM isolation) for untrusted or multi-tenant workloads.
Familiarity with GPU telemetry (DCGM) and GPU utilization optimization.
Experience with lineage and governance contracts (OpenLineage) and AI license management.
Exposure to multi-tenant cost attribution and per-tenant SLA/alerting.
Exposure to regulated delivery environments (financial services, tax, healthcare, risk).

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.