Manager, Software Engineering | OpenShift AI Model Training Team

Job Summary:

Are you ready to join a game-changing open-source AI platform that harnesses the power of hybrid cloud to drive innovation?

The Red Hat OpenShift AI (RHOAI) team is looking for an Associate Engineering Manager with Kubernetes and MLOps (Machine Learning) experience to join our rapidly growing engineering team. Our focus is to create a platform, partner ecosystem, and community by which enterprise customers can solve problems to accelerate business success using AI. This is a very exciting opportunity to build and impact the next generation of hybrid cloud MLOps platforms, help shape the roadmap of the RHOAI product, participate in open source communities, and be at the forefront of the exciting evolution of AI. You’ll join an ecosystem that fosters continuous learning, career growth, and professional development.

In this role, you will manage the model training team in OpenShift AI, based on different upstream projects (Ray, Kubeflow, Pytorch etc). You will build, organize, and manage a global team of talented Red Hat engineers. You will lead the team's work and delivery through participation in the team’s technical design discussions and prioritization of the team’s roadmap in coordination with product management and other external stakeholders. You will grow your associates through coaching, performance reviews, career development planning, and more. You should be a strategic thinker who can align open source initiatives with business goals and foster a collaborative and high-performance team culture.

What you will do:

• Lead and inspire a distributed team of individual contributors, fostering a collaborative and innovative work environment with impact across multiple components of the RHOAI product

• Engage with the Ray and Kubeflow open source communities

• Work with product management, customers, engineering teams, and other stakeholders to formulate a product vision and roadmap as well as an execution plan to achieve this vision

• Work with cross-functional engineering managers and teams on documentation, product management, and quality assurance to coordinate tasks necessary for releasing enterprise-quality MLOps software.

• Evolve cross-team processes as needed to improve execution as the team scales

• Working closely with the technical leads and scrum leads to direct the team in agile development. 

• Mentor and guide team members in their career development, and growth.

• Recruit new associates within the growing engineering team.

What you will bring:

• Experience developing applications in Go or Python, or other language

• Experience in Kubernetes, OpenShift or other cloud-native technologies

• Understanding of core MLOps concepts, particularly in model training/tuning

• Experience in managing machine learning engineering or operations teams, with a sustained track record of successful project delivery and development of software engineers

• Excellent written and verbal communication skills

• Ability to lead and work with diverse and distributed teams from multiple countries and cultures

The following will be considered a plus:

• While a Bachelor’s degree or higher in computer science or a related discipline is valued, we prioritize practical experience and technical prowess

• Understanding of how Open Source and Free Software communities work

• Experience with development for public cloud services (AWS, GCE, Azure)

• Experience working with or deploying MLOps platforms will be a plus

#LI-PM1

About Red Hat

Red Hat is the world’s leading provider of enterprise open source software solutions, using a community-powered approach to deliver high-performing Linux, cloud, container, and Kubernetes technologies. Spread across 40+ countries, our associates work flexibly across work environments, from in-office, to office-flex, to fully remote, depending on the requirements of their role. Red Hatters are encouraged to bring their best ideas, no matter their title or tenure. We're a leader in open source because of our open an