Engineering Leader, AI & Machine Learning Operations, AIOps

Job not on LinkedIn

August 12

🏄 California – Remote

Although this job is listed in California, this company may be open to hiring remote in other states.

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

🦅 H1B Visa Sponsor

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CloudBees

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Software • B2B • DevOps

CloudBees is a leading provider of DevOps solutions, specializing in optimizing and managing the software development lifecycle through its advanced Continuous Integration and Continuous Delivery (CI/CD) capabilities. By leveraging Jenkins, CloudBees enables developers to streamline workflows, automate processes, and enhance software delivery across hybrid and multi-cloud environments. CloudBees empowers organizations to optimize developer experiences through features like security and compliance management, feature management, and smart testing, ensuring seamless integration of tools within the DevOps ecosystem.

501 - 1000 employees

Founded 2010

🤝 B2B

💰 $95M Debt Financing on 2021-12

📋 Description

• Lead and scale a team responsible for AIOps, including model deployment, monitoring, and lifecycle management. • Architect and implement AI/ML pipelines that are scalable, observable, and reproducible. • Collaborate with cross-functional teams (data science, DevOps, product) to integrate AI/ML systems into our SaaS platform. • Establish best practices for AI/ML experimentation, CI/CD for models, data versioning, and model governance. • Own the full stack of AIOps infrastructure, from data ingestion to real-time inference systems. • Drive technical vision and roadmap for ML platform development. • Act as a mentor and coach, helping engineers grow in a fast-paced, startup environment. • Manage a team of 5+ Ability to launch new platforms 0 - 1 and drive adoption internally and externally with partner teams.

🎯 Requirements

• 7+ years of engineering experience, including platform engineering, system development, or related roles with at least 3 years in leadership roles. • 3 years of experience with large-scale systems, with a focus on reliability, scalability, and maintainability; and 1 year of experience with AI/ML systems • Strong hands-on experience with MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Airflow, Metaflow). • Proven track record building ML pipelines in production environments. • Experience with cloud infrastructure (AWS, GCP, or Azure) and container orchestration (Kubernetes). • Deep knowledge of CI/CD practices as they relate to ML lifecycle. • Prior experience in a startup or fast-paced SaaS environment. • Strong collaboration and communication skills. • Experience deploying and managing services such as Amazon bedrock or Vertex AI - LLm

🏖️ Benefits

• Competitive compensation, stock options, and benefits. • A flexible remote work culture with global teammates.