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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.

Design and develop full-stack applications and distributed systems, utilizing Python, Go, React, and Kubernetes while contributing to architectural decisions and rapidly prototyping new capabilities.

The Proposal Solution Architect/Writer will lead proposal execution, writing, and compliance for federal submissions, building proposal processes and infrastructure to enhance quality and efficiency.

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

Design and enforce secure multi-cloud environments, manage Azure Landing Zones, deploy Infrastructure as Code and ensure compliance with regulations while collaborating across technical teams.

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.