Sustainment Technical Product Specialist

KBR, Inc.•Colorado Springs, CO

1d•$133,000 - $167,000•Onsite

About The Position

KBR’s National Security Solutions team provides high-end engineering and advanced technology solutions to our customers in the intelligence and national security communities. In this position, your work will have a profound impact on the country’s most critical role – protecting our national security. KBR (formerly LinQuest) is the prime contractor on the Military Satellite Communications (MILSATCOM) Systems Engineering, Integration and Test (MSEIT) contract. KBR is seeking a highly-skilled, motivated and proactive teammate to support Mission Delta 8 (MD8), 8th Sustainment Squadron (8STS) located at Peterson Space Force Base (PSFB), CO. We conduct 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, providing global secure, survivable and protected communications for our Nation’s warfighters. We are looking for an experienced, dynamic and effective Sustainment Technical Product Specialist for a front-facing role within the Government System Program Office to effectively develop and implement Artificial Intelligence (AI) and Machine Learning (ML) solutions across the MILSATCOM & NC3 O&S portfolio. The ideal candidate will bring their innovative skills and passion to identify where AI-enabled workflows can bring measurable value to the portfolio, prototype those capabilities using modern tools and repeatable workflows, and build the evaluation and verification discipline needed to make those capabilities trustworthy for fielding. The role spans the full capability lifecycle: discovering relevant mission use cases, building practical prototypes and evaluation assets, assessing operational fitness and failure modes, and transferring knowledge to the broader engineering team through documentation, briefings, and mentoring. This position is intentionally scoped to reflect a high-autonomy engineer who can independently research emerging practices, convert learning into usable artifacts, and align AI capability development with mission needs, engineering rigor, and leadership decision-making. This position will be joining our high-functioning, high-performing MSEIT Sustainment IPT and be an important part of directly and positively affecting warfighter readiness as they employ US Space Force (USSF) systems worldwide in support of full spectrum military operations.

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 that can be applied to 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 not only what an AI system says, but what it does, how it does it, what authority it exercises, and what happens when it deviates
Instrument AI systems with distributed tracing and observability practices to support failure diagnosis, drift detection, regression analysis, and black-box evaluation in constrained environments
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 that help other engineering and test personnel extend their skills into the AI domain
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