Lead DevOps Engineer (AI & GPU)

Overview

Lead the DevOps, delivery, model-serving, routing and observability layer of Zimperium's AI-native mobile security platform, spanning cloud, on-prem, edge and air-gapped environments.

What you'll do

• Build and operate CI/CD/CV pipelines that ship AI services, agents and runtime components, including automated build, test, continuous verification, release and rollback.
• 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, and cost attribution and utilization (Apptio/OpenCost/Kubecost), so AI execution stays economically bounded 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.
• Automate GitOps-based delivery and continuous verification, embedding quality, integrity and cost gates into pipelines so releases are policy-compliant by default.
• Close the loop between delivery and observability by using telemetry, evaluation and cost signals to drive deployment decisions, progressive rollout and automated rollback.
• Ensure cost and telemetry are identity-stamped and per-tenant, so consumption and behavior are attributable end-to-end.

What you'll need

• Bachelor's or Master's degree in Computer Science or a related technical field.
• 8+ years in DevOps, MLOps, platform or observability engineering, with hands-on production ownership of AI or high-throughput services.
• Strong DevOps expertise: CI/CD/CV pipeline design, GitOps, continuous verification, and progressive/automated release and rollback for production workloads.
• Strong hands-on DevOps experience with GitOps tooling (ArgoCD, Helm, GitHub Actions/GitLab CI, or equivalents).
• Hands-on expertise operating inference/model-serving frameworks (Ray Serve, vLLM, Triton, or NIM) on GPU infrastructure at production scale.
• 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).
• Familiarity with model/artifact governance, registries, CVE scanning, and license/lineage tracking.
• 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.
• 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.

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).

Details

• Location: Bangalore, India.