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 companys 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 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.

Qualifications:

• Bachelors or Masters 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 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 Have:

• 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).

Zimperium is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to race color religion sex including sexual orientation and gender identity national origin disability protected veteran status or any other characteristic protected by applicable federal state or local law.