The Role
The Cloud Infrastructure team at Kumo is responsible for managing and scaling our Kubernetes-based, cloud-native AI platform across multiple cloud providers. They set service level objectives, optimize resource allocation, enforce security compliance, and drive cost efficiency for the Multi-Cloud Platform.
As a key team member, you will architect and operate a highly scalable, resilient Kubernetes infrastructure to support massive Big Data and AI workloads. You’ll design and implement advanced cluster management strategies, fleet capacity scaling, optimize workload scheduling, and enhance observability at scale. Your expertise in Kubernetes internals, networking, and performance tuning will be critical in ensuring high availability and seamless scaling.
Joining early, you'll play a pivotal role in shaping platform reliability, automating infrastructure, and enabling ML engineers with efficient commit-to-production automation, Continuous Provisioning, CI/CD, ML Ops, and deployment orchestration and workflows. You'll collaborate with ML scientists, product engineers, and leadership to influence scaling strategies, develop self-service tooling, and drive multi-cloud resilience. Engineers at Kumo take ownership of core system design, building infrastructure that powers the next generation of AI applications.
Key Responsibilities
- Design, build, and scale Kubernetes-based infrastructure to support Kumo’s multi-cloud AI platform, ensuring high availability, resilience, and performance.
- Architect and optimize large-scale Kubernetes clusters, improving scheduling, networking (CNI), and workload orchestration for production environments.
- Develop and extend Kubernetes controllers and operators to automate cluster management, lifecycle operations, and scaling strategies.
- Enhance observability, diagnostics, and monitoring by building tools for real-time cluster health tracking, alerting, and performance tuning.
- Lead efforts to automate fleet management, optimizing node pools, autoscaling, and multi-cluster deployments across AWS, GCP, and Azure.
- Define and implement Kubernetes security policies, RBAC models, and best practices to ensure compliance and platform integrity.
- Collaborate with ML engineers and platform teams to optimize Kubernetes for machine learning workloads, ensuring seamless resource allocation for AI/ML models.
- Drive commit-to-production automation, cloud connectivity, and deployment orchestration, ensuring seamless application rollouts, zero-downtime upgrades, and global infrastructure reliability.
Required Skills and Experience
- Kubernetes Mastery: 5-7+ years of experience managing large-scale Kubernetes clusters (EKS, GKE, AKS, or OpenSource) in production. Deep expertise in Kubernetes internals, including controllers, operators, scheduling, networking (CNI), and security policies.
- Cloud-Native Infrastructure: 5-7+ years of experience building cloud-native Kubernetes-based infrastructure across AWS, Azure, and GCP.
- Platform Engineering: 5-7+ years of experience building Kubernetes service meshes (Istio/Envoy, Traefik), networking policies (Calico/Tigera), and distributed ingress/egress control.
- Fleet Management & Scaling: Proven experience in optimizing, scaling, and maintaining Kubernetes clusters across multi-cloud environments, ensuring high availability and performance.
- Software Development: 5-7+ years of experience writing production-grade controllers and operators in Python, Go, or Rust to extend Kubernetes functionality.
- Infrastructure-as-Code & Automation: Hands-on experience with Terraform, CloudFormation, Ansible, BASH and Make scripting to automate Kubernetes cluster provisioning and management.
- Distributed Systems & SaaS: Expertise in building and operating large-scale distributed systems for cloud-native B2B SaaS applications running on Kubernetes.
- Cloud Application Deployment: Deep expertise in building of container orchestration, workload scheduling, and runtime optimizations using Kubernetes, Argo or Flux.
- Education: BS/MS in Computer Science or a related field (PhD preferred)
Nice to Have
- Proficiency with cloud platforms such as AWS, GCP, or Azure.
- Familiarity with chaos engineering tools and practices for testing system resilience.
- Strong understanding of security best practices and compliance standards (GDPR, SOC2, ISO27001, vulnerability assessments, GRC, risk management).
- Contributions to open-source projects, particularly in the Kubernetes or cloud-native ecosystem.
- Expertise in Docker, Kubernetes, Jenkins, Flux, Argo, and Terraform in a Linux environment.
- Hands-on experience with monitoring and observability tools such as Prometheus and Grafana.
- Ability to develop customer-facing web frontends or public APIs/SDKs for platform services.
Benefits
- Competitive salary and equity options.
- Comprehensive medical and dental insurance.
- An inclusive, diverse work environment where all employees are valued and supported.
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Similar Jobs
.png)
Get Personalized Job Insights.
Our AI-powered fit analysis compares your resume with a job listing so you know if your skills & experience align.
Success! Refresh the page to see how your skills align with this role.
The Company
What We Do
Democratizing AI on the Modern Data Stack!
The team behind PyG (PyG.org) is working on a turn-key solution for AI over large scale data warehouses. We believe the future of ML is a seamless integration between modern cloud data warehouses and AI algorithms. Our ML infrastructure massively simplifies the training and deployment of ML models on complex data.
With over 40,000 monthly downloads and nearly 13,000 Github stars, PyG is the ultimate platform for training and development of Graph Neural Network (GNN) architectures. GNNs -- one of the hottest areas of machine learning now -- are a class of deep learning models that generalize Transformer and CNN architectures and enable us to apply the power of deep learning to complex data. GNNs are unique in a sense that they can be applied to data of different shapes and modalities.
