Lead DevOps Engineer (AI & GPU)

Zimperium is a global leader in AI-empowered mobile security, delivering purpose-built protection for mobile applications and devices against advanced threats such as mobile phishing, malware, app vulnerabilities, tampering, device compromise, and zero-day attacks. As cybercriminals increasingly adopt a mobile-first attack strategy, Zimperium enables organizations to detect and stop mobile threats while gaining clear visibility into mobile risk. The company’s autonomous security solutions help safeguard critical mobile infrastructures and the sensitive data of organizations and their customers.

As part of our fast-growing pace, we are seeking an AI Systems Engineer to own the delivery, model-serving, routing, and observability layer of AI-native platform. These are the systems that ship, run, and make fully visible every AI workload. Within the Hybrid AI Multi-Environment Runtime, 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 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. 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 repeatedly and every AI request must be economically bounded, attributable, and traceable end-to-end.

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 repeatedly 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: 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 an...