Staff AI DevOps Engineer

WHAT YOU’LL DO:  

Join the dynamic team at Kaseya as a Staff AI DevOps Engineer. Build the automated backbone of our AI platform, driving CI/CD, Infrastructure as Code (IaC), and observability to enable secure, scalable, and rapid deployment of intelligent agents. You’ll lead the implementation of cutting-edge DevOps, DevSecOps, and MLOps practices that support continuous delivery, resilient infrastructure, and high-performance AI workloads. Your work will ensure our platform is production-ready, cost-efficient, and built for speed, security, and scale.

WHAT WE ARE LOOKING FOR:  

We’re looking for a DevOps engineer who thrives in cross-functional environments and brings a strong sense of ownership to infrastructure reliability and automation. You’re a clear communicator who can collaborate across AI, platform, and security teams, document complex systems, and present infrastructure strategies and metrics to technical leadership. You lead by example, mentor others in best practices, and drive continuous improvement across the stack.

 

THE SCHEDULE: 
This position is 100% remote.

ESSENTIAL DUTIES AND RESPONSIBILITIES: 

• Design and maintain CI/CD pipelines to support rapid, reliable delivery of AI platform components and intelligent agents
• Implement Infrastructure as Code (IaC) and GitOps practices to manage scalable, secure, and reproducible environments
• Build and manage Kubernetes-based infrastructure, including custom operators and GPU workload orchestration
• Integrate MLOps tools and workflows to streamline the AI model lifecycle from training to deployment
• Deploy and maintain observability stacks for real-time monitoring, tracing, and alerting across distributed systems
• Enforce DevSecOps practices, including automated security scanning, policy-as-code, and compliance automation
• Optimize cloud infrastructure for performance, cost-efficiency, and resilience

WHAT YOU’LL BRING:

• 5-7+ years of experience in DevOps, SRE, or platform engineering
• Expert with CI/CD pipeline design and tools (e.g., Jenkins, GitLab CI, ArgoCD, GitHub Actions)
• Deep Infrastructure as Code (IaC) experience (e.g., Terraform, CloudFormation)
• Expert-level Kubernetes administration and workload management, including custom controllers/operators
• Experience with MLOps tools and platforms (e.g., Kubeflow, MLflow, Seldon Core, Vertex AI Pipelines, SageMaker MLOps)
• Proficiency in implementing observability stacks (e.g., Prometheus, Grafana, Jaeger, OpenTelemetry, ELK)
• Advanced scripting skills (Python, Bash, Go)
• Strong understanding of cloud security best practices and automation (e.g., SAST, DAST)
• Experience with GitOps workflows and cost optimization strategies for cloud-based AI/ML workloads
• Bachelor's degree in Computer Science, DevOps Engineering, or a related field
• Preferred: Certified Kubernetes Administrator (CKA), DevOps Engineer certifications (AWS, Azure, GCP), or FinOps certification
• Additional certifications in MLOps platforms or cloud security are a plus

Nice to Have:

• Experience with GPU cluster management and scheduling (e.g., Slurm, NVIDIA AI Enterprise, MIG)
• Infrastructure for federated learning or edge AI
• Proficiency in policy-as-code frameworks (e.g., Open Policy Agent, Kyverno)
• AI model serving optimization and inference acceleration (e.g., TensorRT, ONNX Runtime)
• Experience building and managing serverless infrastructure for AI
• FinOps certification or strong practical experience