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JFrog helps the world’s largest companies deliver software and AI at scale. JFrog ML unifies AI/ML, Security, and DevOps, providing organizations with a trusted platform to manage their entire AI and ML workflows.
At JFrog ML, we support data science teams at every stage of the model lifecycle, from training to serving, with advanced security, governance and deployment capabilities across cloud and self-hosted environments. Our platform streamlines data engineering, enabling teams to ingest, transform and store data for their ML initiatives. We’re looking for a DevOps Team Lead to lead a team responsible for the infrastructure, automation, and reliability behind JFrog’s next-generation AI/ML platform.
As a DevOps Team Lead at JFrog ML, you will...- Lead the DevOps team for JFrog ML and set strategy for infrastructure, release engineering, and system reliability
- Build and manage a multi-cloud infrastructure (AWS/GCP/Azure) using Infrastructure-as-Code and Kubernetes
- Ensure observability across all services — monitoring, logging and alerting — using tools such as Prometheus, Grafana, and ELK
- Partner with software engineers, product teams, and security to enforce governance, compliance, and performance standards
- Build internal tools and automation to streamline developer workflows and accelerate experimentation
- Hire, mentor, and grow DevOps engineers focused on innovation and operational excellence
- 7+ years of experience in DevOps, SRE, or infrastructure engineering, with 2+ years in a team lead role
- Expertise in Kubernetes, Helm, and cloud-native deployments
- Working knowledge in at least two of the main cloud providers (AWS\GCP\Azure) and experience with IAC tools like Terraform
- Strong scripting and automation skills (Python, Bash, or Go)
- Proven ability to scale infrastructure and improve reliability in fast-paced SaaS or platform environments
- Passion for creating great developer experiences through self-service tooling and automation.
- Excellent leadership, communication, and collaboration skills
- Hands-on experience with MLOps or GenAI workflows - An advantage
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