About This Opportunity
Zimperium is a global leader in AI-empowered mobile security, delivering purpose-built protection for mobile applications and devices against advanced thre
This listing is organized to help candidates compare the role, location, experience level and application source before visiting the employer or source website. Final eligibility, salary, selection process and deadlines should be confirmed from jobs.lever.co.
Skills And Responsibilities
- 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
How To Apply
- Read the summary, experience requirement, location and deadline on this page.
- Open the official application/source link for the final employer instructions.
- Prepare an updated resume, education details, work samples if relevant, and identity documents only when requested by the official process.
- Apply only through the official source. Do not pay registration fees or share OTPs, banking passwords or UPI PINs.
Eligibility Notes
Candidates should compare their qualification, experience, skills, location preference and availability with the official job description. If a requirement is not clearly mentioned here, treat it as employer-specific and verify it on the source page.
Candidate Guidance
Lead Devops Engineer (AI & GPU) at Zimperium should be evaluated by matching the role title, category, location and experience requirement with your resume before applying.
- Candidate fit: this DevOps Jobs is best for applicants who can show relevant skills, projects or work experience connected to the role.
- Experience check: compare your profile with 8+ yrs; if you are a fresher, highlight projects and internships, and if experienced, show measurable work outcomes.
- Location/work mode: verify whether Bangalore, India is onsite, hybrid, remote, rotational shift or relocation-based on the official source page.
- Application safety: use the official application/source link, avoid registration fees and do not share OTPs, bank details or original documents through unofficial channels.
Before Applying, Check
- Does my resume clearly show the skills required for this role?
- Have I checked the official source page for the latest eligibility and location details?
- Do I understand the interview process, work mode and joining requirements?
- Is the application link asking only for normal job-application information?
ManaCoding Jobs is a job-discovery platform. We organize role information and source links, but candidates should verify final details on the official employer or source page before applying. Salary is shown only when available from the listing/source; otherwise it is marked as not disclosed.