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Zimperium is a global leader in AI-empowered mobile security, protecting organizations against advanced mobile threats including phishing, malware, vulnerabilities, tampering, and zero-day attacks. The company is seeking an AI Systems Engineer to own the delivery, model-serving, routing, and observability layer of an AI-native platform.
In this role, you will architect and operate the systems that ship, run, and provide full visibility into every AI workload. You will own how AI services and agents are built and deployed, how models execute, how requests are routed, how AI assets are catalogued and governed, how consumption is measured and bounded, and how the entire platform is observed across cloud, on-premises, edge, and air-gapped environments.
Key responsibilities include:
- Building and operating CI/CD/CV pipelines that ship AI services, agents, and runtime components with automated build, test, continuous verification, release, and rollback
- Owning governance and discovery for AI assets, including service catalogs, model registries, experiment tracking, CVE/SBOM scanning, and license management
- Managing resource allocation and cost attribution per tenant, ensuring AI execution remains economically bounded and controllable
- Designing and operating a comprehensive observability stack including metrics, logs, traces, dashboards, and LLM debugging tools
- Implementing OpenTelemetry collection with multi-tenant receivers, GPU telemetry exporters, and dynamic filtering
- Automating GitOps-based delivery with quality, integrity, and cost gates embedded into pipelines
- Using telemetry and cost signals to drive deployment decisions, progressive rollout, and automated rollback
- Ensuring cost and telemetry are identity-stamped and per-tenant for end-to-end attribution
You will work at the intersection of DevOps, MLOps, and platform engineering, building systems that are production-grade, cost-aware, observable, and compliant by design.
Requirements:
- Bachelor's or Master's degree in Computer Science or related technical field
- 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 with CI/CD/CV pipelines and GitOps tooling (ArgoCD, Helm, GitHub Actions, GitLab CI, or equivalents)
- Hands-on expertise operating inference/model-serving frameworks (Ray Serve, vLLM, Triton, 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 and 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
- Strong communication skills to explain runtime, cost, and observability tradeoffs to engineers, architects, and leadership
- Comfortable operating across cloud, on-premises, edge, and air-gapped environments with consistent runtime and telemetry semantics
Nice to have:
- Experience with LLM evaluation and debugging tooling (LangSmith, Langfuse) and prompt/response quality measurement
- Experience with sandboxed/secure execution (gVisor, Firecracker, 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)
