{"id":2461662,"url":"https://alion.io/job/zimperium-lead-devops-engineer-ai-gpu","title":"Lead DevOps Engineer (AI & GPU)","company":{"id":2907,"name":"Zimperium","domain":"zimperium.com","url":"https://alion.io/company/zimperium","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":95,"open_postings":15,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":140,"computed_at":"2026-10-14T06:34:16Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":25000,"max_usd":57000,"period":"year","method":"role_seniority_country_cell","sample_n":34},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Apache Kafka","optional":false},{"name":"ArgoCD","optional":false},{"name":"CI/CD","optional":false},{"name":"CVE","optional":false},{"name":"FinOps","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GitLab CI","optional":false},{"name":"GitOps","optional":false},{"name":"Grafana","optional":false},{"name":"Harbor","optional":false},{"name":"Hugging Face","optional":false},{"name":"Jaeger","optional":false},{"name":"JFrog Artifactory","optional":false},{"name":"Langfuse","optional":false},{"name":"LangSmith","optional":false},{"name":"LLM","optional":false},{"name":"Loki","optional":false},{"name":"Mimir","optional":false},{"name":"MLFlow","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"Prometheus","optional":false},{"name":"Ray","optional":false},{"name":"Ray Serve","optional":false},{"name":"SBOM","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"Triton","optional":false},{"name":"Trivy","optional":false},{"name":"vLLM","optional":false},{"name":"Kubernetes","optional":true}],"status":"live","first_seen_at":"2026-10-13T16:01:05Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-10-14T05:47:18Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"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.\nAs part of our fast-growing pace, weare 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, howmodels 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.\nThis 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.\nThis 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.\nResponsibilities:\nOwn 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.\nOwn 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.\nOwn 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.\nOwn 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.\nOwn 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.\nAutomate 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.\nClose 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.\nEnsure 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.\nRequirements:\nStrong DevOps expertise: CI/CD/CV pipeline design, GitOps, continuous verification, and progressive/automated release and rollback for production workloads.\nDeep expertise operating model-serving and inference systems (Ray, vLLM/Triton/NIM) on GPUs at production scale.\nDeep observability skills: metrics, logs, traces, and OpenTelemetry.\nFinOps mindset: able to attribute, bound, and optimize AI consumption cost per tenant and workload.\nFamiliarity with model/artifact governance, registries, CVE scanning, and license/lineage tracking.\nComfortable operating across cloud, on-prem, edge, and air-gapped environments with consistent runtime and telemetry semantics.\nStrong communicator able to explain runtime, cost, and observability tradeoffs to engineers, architects, and leadership.\nQualifications:\nBachelor’s or Master’s degree in Computer Science or related technical field.\n8+ years in DevOps, MLOps, platform, or observability engineering, with hands-on production ownership of AI or high-throughput services.\nStrong 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.\nHands-on expertise operating inference/model-serving frameworks (Ray Serve, vLLM, Triton, or NIM) on GPU infrastructure.\nStrong experience with observability stacks (Prometheus, Grafana, Loki, Tempo/Jaeger) and OpenTelemetry.\nExperience with API gateways and request routing (Envoy or equivalent), including streaming responses.\nExperience with cost management / FinOps tooling (OpenCost, Kubecost, or equivalent) and quota/rate-limit enforcement.\nFamiliarity with model/artifact registries and supply-chain scanning (Harbor, MLflow, Trivy/SBOM).\nProven track record operating AI or service infrastructure under compliance, security, or regulatory constraints.\nAbility to define clean ownership boundaries and consumption contracts with platform, trust, and data teams.\nNice to Have:\nExperience with LLM evaluation and debugging tooling (LangSmith, Langfuse) and prompt/response quality measurement.\nExperience with sandboxed/secure execution (gVisor, Firecracker, or microVM isolation) for untrusted or multi-tenant workloads.\nFamiliarity with GPU telemetry (DCGM) and GPU utilization optimization.\nExperience with lineage and governance contracts (OpenLineage) and AI license management.\nExposure to multi-tenant cost attribution and per-tenant SLA/alerting.\nExposure to regulated delivery environments (financial services, tax, healthcare, risk).\nZimperium is an Equal Opportunity employer. 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