Jobs at Rackner

The Senior Technical Account Manager will advise customers on adopting a Kubernetes-based DevSecOps platform, drive platform adoption, educate users, and maintain customer engagement.

The Software Engineer will design and develop cloud-native applications, collaborate with teams, and optimize systems in a secure environment for national defense purposes.

Design and operate Kubernetes-based platforms for AI/ML systems, manage containerized environments, and develop CI/CD pipelines for secure deployment in mission-critical settings.

Lead the capture lifecycle in the DoD sector, developing strategies, proposals, and alliances. Build relationships with decision-makers and represent the company at events.

As a Computer Science Intern, you will assist in software development, conduct research, troubleshoot issues, and collaborate with teams on projects.

The R&D Engineer will build and refine software solutions for defense use cases, prototype systems, and convert stakeholder feedback into technical requirements. Collaboration with engineering teams and customer-oriented engagements are key responsibilities.

Lead the design and development of mission-relevant software, build backend services and APIs, mentor engineers, and support technical demos.

The role involves supporting a US Air Force program by building and maintaining a DevSecOps platform, focusing on Kubernetes clusters, Terraform, and Helm charts, while ensuring compliance with cybersecurity policies.

Fellows will collaborate in teams to design and prototype AI/ML and cloud solutions, conducting research on emerging technologies and presenting prototypes to leadership.

Develop and deliver solutions in a DevSecOps environment using cloud-native technologies, focusing on containerized applications, CI/CD pipelines, and data services.

The MLOps Engineer will manage the ML system lifecycle, deploy models in mission-critical environments, and ensure reliability through monitoring and infrastructure optimization. They will work with cloud-native technologies and focus on operationalizing AI/ML capabilities within classified settings.

The AI/ML Engineer will design and deploy machine learning solutions, build model architectures, collaborate with teams, and ensure model performance and scalability.