AI Systems Engineer - DevOps& Observability - Senior

EY is a globally connected professional services organization focused on building a better working world through assurance, consulting, tax, strategy, transactions, data, AI, and advanced technology. The company is seeking a Senior AI Systems Engineer to own delivery, model-serving, routing, governance, cost management, and observability for AI workloads across cloud, on-premises, edge, and air-gapped environments. The role builds automated DevOps and GitOps pipelines, operates production inference systems, and ensures AI workloads are traceable, economically bounded, and compliant.

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

Requirements:

• • 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
• • 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)