Software Engineer Lead - Cloud Engineering

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 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

Nice to Have

Benefits

$175,000 - $250,000 a year

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.