Key responsibilities

• Lead and mentor a team of DevOps engineers, fostering technical growth and collaboration

• Define and drive the infrastructure roadmap aligned with company objectives

• Architect and oversee cloud infrastructure design and implementation

• Establish best practices, standards, and processes for infrastructure development and operations

• Partner with Engineering, Research, and FDE to align infrastructure capabilities with business needs

• Drive the evolution of Kubernetes clusters optimized for GPU workloads, Production SaaS hosting and and varied enterprise deployment models

• Champion GitOps practices using ArgoCD for continuous deployment

• Establish infrastructure as code standards using Terraform

• Define monitoring and observability strategy for distributed systems

• Collaborate with ML engineers to optimize infrastructure for model training and serving

• Own infrastructure reliability, performance, and security posture

• Implement and maintain cost optimization strategies (FinOps) for cloud resources

Must have

• 7+ years of experience in cloud infrastructure and DevOps, with 5+ years in a technical leadership role

• Proven track record of building and leading high-performing infrastructure teams

• Strong experience with AWS, GCP and Azure

• Deep expertise in Kubernetes, including multi-cluster management, GPU workload optimization, resource scheduling and autoscaling, and network policies and security

• Extensive experience with cloud networking, including VPC design, load balancer configuration, network security and segmentation, and cross-cloud networking solutions

• Strong CI/CD expertise, preferably with GitHub Actions

• Proficiency in Terraform

• Proficiency with GitOps tools (ArgoCD preferred)

• 3+ years of experience with Python

• Experience with monitoring and observability tools

• Experience with FinOps practices and cloud cost optimization

• Excellent communication skills with ability to translate technical concepts for diverse audiences

Nice to have

• Experience with ML workflow tooling (MLflow, Kubeflow, or similar)

• Experience with FastAPI and backend applications

• Familiarity with data platforms like Databricks or Snowflake

• SRE practices experience or cloud security certifications

• Hands-on experience with Prometheus, Grafana, or Datadog

• Experience scaling infrastructure for AI/ML startups

Benefits

• Competitive compensation with salary and equity