Sustainment Technical Product Specialist

KBR•Colorado Springs, CO

1d•Onsite

About The Position

KBR's National Security Solutions team is seeking a highly-skilled, motivated, and proactive Sustainment Technical Product Specialist to support Mission Delta 8 (MD8), 8th Sustainment Squadron (8STS) at Peterson Space Force Base (PSFB), CO. This role involves operations and sustainment (O&S) activities for state-of-the-art Military and Commercial Satellite Communications (SATCOM) and Nuclear Command, Control and Communications (NC3) systems. The specialist will be responsible for developing and implementing Artificial Intelligence (AI) and Machine Learning (ML) solutions across the MILSATCOM & NC3 O&S portfolio. This includes identifying AI-enabled workflow opportunities, prototyping capabilities, and building evaluation and verification disciplines. The role covers the full capability lifecycle, from use case discovery to knowledge transfer. This position requires a high-autonomy engineer capable of independent research, converting learning into usable artifacts, and aligning AI development with mission needs and engineering rigor. The successful candidate will join the MSEIT Sustainment IPT and contribute to warfighter readiness.

Requirements

Active DoW Top Secret clearance with SCI eligibility
Bachelor’s degree in computer science, software engineering, information technology or related discipline
10+ years of experience in planning, executing, or leading Sustainment Test and Evaluation activities for complex systems in a defense, government, or other high-consequence environment
Experience in defense, government, or other high-security/regulatory environments, working with military and DoW civilians
Demonstrated experience working with military SATCOM systems, such as Milstar, AEHF, EPS, GBS
Familiarity working on AI/ML or generative AI products, including exposure to LLMs, prompt engineering, or RAG-based solutions
Familiarity with integrating AI solutions into enterprise systems, data platforms, and APIs
Strong expertise in Agile methodologies (Scrum/Kanban), including leading sprint planning, backlog refinement, daily standups, and sprint reviews
Hands-on experience managing backlogs, epics, and user stories using Azure DevOps, Jira, and Confluence
Exposure to product ownership, technical product management, or program/project management
Experience tracking delivery progress, managing risks, and ensuring timely delivery of product milestones
Proven ability to define product vision, roadmap, and detailed use cases aligned to business and mission objectives
Demonstrated ability to translate complex technical concepts (AI/ML, APIs, cloud systems) into clear, actionable product requirements
Experience managing cross-functional teams and coordinating dependencies across engineering, data, and business stakeholders
Strong stakeholder management skills, including requirements gathering, prioritization, and executive-level communication and reporting
Possess a cyber security certification such as CompTIA Security+ and/or CompTIA A+

Nice To Haves

Familiarity with cloud platforms (e.g., Azure) and modern application architectures, including APIs and containerized environments
Proven ability to define success metrics, support testing/validation, and oversee release planning and quality assurance
Strong facilitation skills for backlog grooming, prioritization workshops, and stakeholder reviews
Exposure to DevOps practices, CI/CD pipelines, and cloud-native deployment environments
Understanding of the Software Development Life Cycle (SDLC), from ideation through deployment and sustainment
Possess International Council of Systems Engineering (INCOSE) certification (e.g., CSEP, ESEP)
Agile certifications such as Certified Scrum Product Owner (CSPO), Professional Scrum Product Owner (PSPO), or SAFe Product Owner/Product Manager

Responsibilities

Develop and maintain a practical roadmap for agentic AI and AI/ML evaluation capabilities for the MSEIT Sustainment Test and Evaluation effort.
Conduct structured applied learning in AI/ML, LLMs, RAG, agents, evaluation frameworks, tracing, safety, reliability, and related software practices; convert learning into usable artifacts.
Design, build, and maintain evaluation harnesses for LLM-enabled and agentic AI applications, including golden test sets, rule-based and model-based graders, human-validated scoring approaches, CI-integrated regression gates, and evidence packages.
Define evaluation claims, measures, acceptance criteria, datasets, rubrics, test conditions, traces, and readiness evidence for AI-enabled systems.
Evaluate AI system behavior, authority exercised, and deviations from expected performance.
Instrument AI systems with distributed tracing and observability practices to support failure diagnosis, drift detection, regression analysis, and black-box evaluation.
Conduct adversarial and resilience-oriented assessments of agentic systems, including prompt injection, goal hijacking, tool misuse, memory poisoning, and related failure modes; document findings and mitigation outcomes.
Help distinguish AI-system failure from test-case defects, evaluator defects, environment failures, configuration problems, and insufficient evidence.
Translate reproducibility, auditability, evaluation integrity, resilience, operational recovery, safety assurance, and human oversight into testable engineering requirements and practical contract guidance.
Map established T&E practices to AI evaluation and create roadmaps, playbooks, demonstrations, templates, and guidance for other engineering and test personnel.
Identify mission-relevant AI/ML use cases and provide evidence-based recommendations concerning value, limitations, risk, readiness, and operational implications.
Produce error taxonomies, technical reports, executive briefings, reference material, and decision-quality evidence for leadership and engineering stakeholders.
Coordinate with systems engineering, software, cybersecurity, data, product, program, and customer stakeholders to ensure evaluations reflect mission context and operational risk.