3 hours ago
Vancouver, CanadaStaff+
Responsibilities
- Architect, design, and develop horizontally scalable, containerized, fault-tolerant services on Kubernetes for enterprise AI and LLM workloads.
- Build and operate high-performance, low-latency inference and platform services for Generative AI and Agentic AI applications.
- Design and optimize distributed systems, storage, networking, and low-level infrastructure components.
- Develop multi-tenant platform services for on-premises, hybrid, and cloud-based AI deployments.
- Design scalable observability architectures and diagnose production issues using monitoring and observability platforms.
- Build and maintain CI/CD pipelines and deployment automation.
- Implement LLM serving capabilities including request routing, rate limiting, token streaming, load balancing, quota management, and usage budgeting.
- Collaborate with product management, AI, and software engineering teams across globally distributed locations.
- Contribute across architecture, design, development, testing, experimentation, performance analysis, deployment, and operations.
- Review code and design documents, provide technical feedback, and help shape the Enterprise AI Platform's technical direction.
Requirements
- 8+ years of experience developing maintainable, modular, resilient, fail-safe, and long-lived software products in a product development organization.
- Strong foundations in data structures, algorithms, operating systems, networking, and distributed systems.
- Hands-on experience with Docker, Kubernetes, and cloud-native architectures.
- Production backend development experience with Go, Python, C++, or Rust.
- Experience owning CI/CD pipelines and release automation end-to-end.
- Strong understanding of datacenter compute, storage, networking, and virtualization.
- Experience deploying software across on-premises, cloud, and hybrid environments.
- Experience designing and tuning high-performance, performance-sensitive system software.
- Understanding of distributed computing, distributed data stores, and large-scale service architectures.
- Experience diagnosing production performance issues with observability and monitoring platforms such as Prometheus, Grafana, Datadog, or OpenTelemetry.
- Familiarity with LLM serving concepts, including rate limiting, token streaming, request scheduling, load balancing, quota management, and usage budgeting.
- Familiarity with LLM reasoning workflows, tool calling, prompt templates, and agents.
- Experience building multi-tenant services on virtualized or containerized infrastructure.
- Strong communication, collaboration, and problem-solving skills for globally distributed teams.
- Master's degree in Computer Science or equivalent practical experience.
- Experience with PyTorch, TensorFlow, GPU acceleration, vLLM, DeepSpeed, Hugging Face TGI, Triton, RAG, vector databases, AI orchestration frameworks, open-source contributions, production AI platforms, LLM APIs, agentic systems, or inference infrastructure is preferred.
Benefits
- Hybrid work arrangement with a minimum of 3 days onsite per week in applicable locations, including Vancouver.
- RRSP with dollar-for-dollar matching up to 7% of base salary.
- Mental health coverage and paramedical benefits.
- Fully paid maternity and parental leave and generous bereavement leave.
- RSUs and an Employee Stock Purchase Plan with a 15% discount.
Categories
About Nutanix
Nutanix is a global leader in cloud software, offering organizations a single platform for running apps and data across clouds. With Nutanix, companies can reduce complexity and simplify operations, freeing them to focus on their business outcomes. Building on its legacy as the pioneer of hyperconverged infrastructure, Nutanix is trusted by companies worldwide to power hybrid multicloud environments consistently, simply, and cost-effectively. Learn more at www.nutanix.com or follow us on social media @nutanix.