Job description

Title: Ai Platform Architect Program Summary KBR’s Missile, Aviation, and Ground Systems (MAGS) division delivers mission engineering solutions for critical U.S. Army programs, specializing in aviation and ground systems, integrated air and missile defense, and threat and target systems. As a trusted partner of the U.S. Department of Defense, MAGS provides innovative, technology-driven solutions to enhance national security. With a global presence and a strong ethical framework, KBR ensures secure, effective, and mission-ready capabilities worldwide. Job Summary As an AI Platform Architect, you will play a critical role in designing, building, and sustaining a secure, on-premises AI platform within a classified environment. This unique opportunity places you at the forefront of enabling advanced AI capabilities for software developers, analysts, and program managers supporting mission-critical initiatives. In this role, you will lead the deployment of a classified High-Performance Computing (HPC) environment to create scalable, secure, and resilient AI infrastructure. You will work closely with Huntsville Site Program Management and Technical Management leads in constructing and deploying all necessary infrastructure to host on-premises AI models and DevSecOps resources utilizing a high-performance computing cluster. This includes the architecting, deployment, and sustainment of HPC operations, networking, on-premises cloud platforms, classified data partitioning and segregation, AI model hosting and lifecycle management, and multi-tenant DevSecOps ecosystems. In partnership with Program, Technical, and Information Assurance (IA) stakeholders, you will ensure compliance with security requirements and lead efforts to achieve and maintain Authority to Operate (ATO) accreditation for mission-critical AI capabilities. Key Responsibilities Work across multiple technology stacks and software tools to build, deploy and maintain on-premises AI multi-tenant hosting. Work across multiple technology stacks and software tools to build, deploy and maintain on-premises DevSecOps multi-tenant hosting. Lead the architecture, deployment, and sustainment of networking infrastructure supporting classified AI and DevSecOps environments. Utilize existing and future High Performance Computing Hardware to determine appropriate resource utilization for multi-tenant AI models and DevSecOps infrastructure Interface with Program Managers and Technical Experts to implement and maintain appropriate AI data segregation across classified programs. Interface with Information Assurance to ensure security compliance to achieve and maintain ATO accreditation. Serve as the lead classified DevSecOps interface between unclassified software development and classified development. Serve as the technical authority for classified AI platform architecture, infrastructure modernization, and platform scalability initiatives. Stay current with software industry best practices, including use of AI in software development, cloud technologies, and evolving security threats to recommend innovative solutions. Basic Qualifications: U.S. Citizenship is required. Active/current Secret Security Clearance is required. Possess a Bachelor of Science degree in Computer Science or other STEM related fields (Engineering, Physics, Mathematics, CIS, etc.) with 10 years of experience or a master’s degree in computer science or related STEM fields with 5 years of experience. Experience with DevSecOps infrastructure and tools Experience with on-premises AI infrastructure and tools Experience with handling vector data from structured and unstructured sources (Microsoft products, binary files, software development files, databases) Deep knowledge about mechanics of building AI platform as a service, agent to agent orchestration, agentic layer integration, AI UX layer and conversational AI models. Linux knowledge is a must Preferred Qualifications: Experience deploying and scaling LLM inference platforms using vLLM, NVIDIA NIM or similar technologies. Strong architecture skills in Distributed Systems, Data Engineering, Cloud Architectures and Platform Engineering. Experience in providing clear guidance on designing secure and regulated AI platforms at local laboratory scale. Experience designing RMF compliant systems (e.g. RMF, NIST 800-53, NIST AI RMF, ICD 503, DISA STIGs, and DoD Zero Trust requirements) Demonstrated experience with NVIDIA GPU architectures (H100, H200, A100, RTX, DGX platforms, etc.) and distributed computing environments Experience with GPU resource scheduling and optimization for multi-tenant AI models Experience deploying and operating vector databases including OpenSearch, Elasticsearch, or equivalent for data segregation and information flow enforcement. Experience implementing MLOps platforms including model lifecycle management, evaluation pipelines, model registries, and continuous AI delivery practices. Experience with data governance, metadata systems, semantic layers and knowledge graphs. Experience deploying and scaling container orchestration tools Experience deploying and scaling security scanning tools like Trivy, SonarQube, Checkmarx. Experience with source code management tools like Git, AzureDevOps, GitHub Experience with CI/CD Tools Experience deploying and scaling Identity & Access Management (IAM) with role-based and attribute-based access for local laboratory scale. Belong, Connect and Grow at KBR At KBR, we are passionate about our people and our Zero Harm culture. These inform all that we do and are at the heart of our commitment to, and ongoing journey toward being a People First company. That commitment is central to our team of team’s philosophy and fosters an environment where everyone can Belong, Connect and Grow. We Deliver – Together. KBR is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, disability, sex, sexual orientation, gender identity or expression, age, national origin, veteran status, genetic information, union status and/or beliefs, or any other characteristic protected by federal, state, or local law.

Requirements

Must-haves

Ai Platform Architect
Program Summary
Work across multiple technology stacks and software tools to build, deploy and maintain on-premises AI multi-tenant hosting.
Work across multiple technology stacks and software tools to build, deploy and maintain on-premises DevSecOps multi-tenant hosting.
Lead the architecture, deployment, and sustainment of networking infrastructure supporting classified AI and DevSecOps environments.
Utilize existing and future High Performance Computing Hardware to determine appropriate resource utilization for multi-tenant AI models and DevSecOps infrastructure
Interface with Program Managers and Technical Experts to implement and maintain appropriate AI data segregation across classified programs.
Interface with Information Assurance to ensure security compliance to achieve and maintain ATO accreditation.
Serve as the lead classified DevSecOps interface between unclassified software development and classified development.
Serve as the technical authority for classified AI platform architecture, infrastructure modernization, and platform scalability initiatives.
Stay current with software industry best practices, including use of AI in software development, cloud technologies, and evolving security threats to recommend innovative solutions.
U.S. Citizenship is required.

Nice-to-haves

Experience deploying and scaling LLM inference platforms using vLLM, NVIDIA NIM or similar technologies.
Strong architecture skills in Distributed Systems, Data Engineering, Cloud Architectures and Platform Engineering.
Experience in providing clear guidance on designing secure and regulated AI platforms at local laboratory scale.
Experience designing RMF compliant systems (e.g. RMF, NIST 800-53, NIST AI RMF, ICD 503, DISA STIGs, and DoD Zero Trust requirements)
Demonstrated experience with NVIDIA GPU architectures (H100, H200, A100, RTX, DGX platforms, etc.) and distributed computing environments
Experience with GPU resource scheduling and optimization for multi-tenant AI models
Experience deploying and operating vector databases including OpenSearch, Elasticsearch, or equivalent for data segregation and information flow enforcement.
Experience implementing MLOps platforms including model lifecycle management, evaluation pipelines, model registries, and continuous AI delivery practices.
Experience with data governance, metadata systems, semantic layers and knowledge graphs.
Experience deploying and scaling container orchestration tools
Experience deploying and scaling security scanning tools like Trivy, SonarQube, Checkmarx.
Experience with source code management tools like Git, AzureDevOps, GitHub

Application prep

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Keywords to mirror

aillmci/cd

Platform: workday
Effort: long

Benefits

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