Software Engineer Lead - Cloud Engineering

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 r 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.

• 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
  .

• Kubernetes Mastery
  : 8-10+ years of experience managing large-scale Kubernetes clusters (EKS, GKE, AKS, or Open Source) in production. Deep expertise in Kubernetes internals
  , including controllers, operators, scheduling, networking (CNI), and security policies
  .
• Cloud-Native Infrastructure
  : 8-10+ years of experience building cloud-native Kubernetes-based infrastructure across AWS, Azure, and GCP.
• Platform Engineering
  : 8-10+ 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
  : 8-10+ 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, Cloud Formation, 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)

• Proficiency with cloud platforms such as AWS, GCP, or Azure.
• Familiarity with chaos engineering tools and…