Software Engineer 4 - Kubernetes AI Platform Engineering
Role Overview
Nutanix is expanding the Nutanix Kubernetes Platform to support enterprise AI, GPU infrastructure, and enterprise applications.
The engineer will architect, build, and scale infrastructure for AI/ML workloads and large-scale distributed systems.
The role reports to a Senior Engineering Manager and does not require travel.
Key Skills for This Role
Full Job Posting
Role overview
Nutanix is expanding the Nutanix Kubernetes Platform to support enterprise AI, GPU infrastructure, and enterprise applications.
The engineer will architect, build, and scale infrastructure for AI/ML workloads and large-scale distributed systems.
The role reports to a Senior Engineering Manager and does not require travel.
Platform engineering responsibilities
- Develop Kubernetes CRDs, Operators, and Controllers in Golang to orchestrate AI workloads.
- Build GPU-aware scheduling, resource isolation, and workload orchestration for AI-native infrastructure.
- Integrate and manage Kubeflow, KServe, Ray, vLLM, and Triton Inference Server within the NKP stack.
- Drive architecture reviews, coding standards, and mentorship for junior and mid-level engineers.
- Collaborate with Product Managers, NKP Kubernetes, and Nutanix Data Services on data gravity and persistent storage.
Required experience and skills
- 6–8 years of software engineering experience, including at least 3 years in scalable distributed systems, cloud platforms, or AI/ML infrastructure.
- Deep expertise in Golang and Python.
- Extensive Kubernetes internals experience, including Operators, Controllers, and Cluster API.
- Hands-on MLOps and distributed AI computing experience, such as Kubeflow, Ray, KServe, or PyTorch DDP.
- Strong understanding of Linux internals, networking, and container runtimes such as containerd or Docker.
- Proven experience designing developer-friendly APIs and scalable microservices architectures.
- Bachelor’s or Master’s degree in a related field, or equivalent practical experience.
Preferred qualifications
- CNCF or AI open-source contributions are a plus.
- Vector database and RAG familiarity is a plus.
- Prometheus, Grafana, or OpenTelemetry experience for AI metrics is a plus.
Work arrangement
- The role is described as hybrid. In listed locations including Vancouver, employees are expected to work onsite at least 3 days per week; team guidance may vary.
- The role does not require travel.
Pay
- Expected base pay is CAD $151,000–$227,000 per year; additional compensation may include a sign-on bonus, restricted stock units, or discretionary awards.
Benefits
- Vancouver benefits include RRSP matching, mental health and paramedical coverage, paid parental leave, equity programs, holidays, sick days, wellness days, and vacation starting at 10 days.
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