Health AI Systems Architect

Booz Allen Hamilton•Washington, DC

About The Position

Health AI Systems Architect The Opportunity: To achieve an organization’s mission, leaders need strong team members who can create and analyze processes, communicate requirements, and develop innovative solutions throughout the execution of the mission. Whether reviewing program-wide technical architecture or providing technical input on synthetic data strategies, our clients need someone who combines deep technical understanding of health data and AI systems with strong architectural judgment, and who can translate complex technical tradeoffs into clear, actionable guidance for program leadership and partners. That is why we need an experienced Health AI Systems Architect like you who can operate at a system-of-systems level to support clients in advancing AI-enabled systems in healthcare. As a part of our team, you'll serve as a strategic technical architect to the Advanced Research Projects Agency for Health (ARPA-H), helping conceptualize, create, and execute advanced government-funded research and development programs to accelerate better health outcomes for everyone. Work with world-class scientists and engineers to support the development of high-impact solutions to society's most challenging health problems. Leverage technical expertise to provide strategic assessments of new technologies in support to senior ARPA-H decision-makers. Maintain responsibility for producing and presenting findings and recommendations to a team of colleagues and clients on the feasibility and potential impact of future research programs, assisting with the management of current programs, and facilitating commercialization of successfully developed technologies. You'll serve as a Health AI Systems Architect advising program leadership and external performers on how technical components fit together across the full lifecycle of the program. You will support clients in ensuring that program-wide technical architecture, spanning data, infrastructure, governance, and security, supports rigorous AI development, evaluation, and long-term impact. Your attention to detail, flexibility, communication skills, understanding of the client's mission, and problem-solving will enable the mission's success.

Requirements

7+ years of experience designing, evaluating, or overseeing complex data-intensive systems
Experience designing or evaluating data platforms, data commons, or shared research infrastructure
Experience with healthcare data types such as EHR, imaging, genomic, or multimodal, and their architectural implications
Experience assessing system-level infrastructure, computing, storage, access control, and scalability tradeoffs
Experience reviewing technical architecture and system designs
Experience producing and presenting high-quality technical documentation and presentations in a fast-paced, dynamic environment
Knowledge of data governance, privacy, and security principles for health data
Master’s degree in a CS, Biomedical Informatics, Systems Engineering, or Data Science field

Nice To Haves

Experience with synthetic data generation and validation in healthcare or biomedical contexts
Experience with health data interoperability standards and their application in large-scale data systems
Experience in technical advisory or evaluation roles
Experience working across multi-institution or multi-performer programs
Experience working in early-stage, fast-paced, and startup environments
Ability to be well-organized, detail-oriented, and adept at multitasking and prioritizing responsibilities to meet deadlines
Doctorate degree in CS, Biomedical Informatics, Systems Engineering, Data Science, or a related field

Responsibilities

Provide program-wide architectural guidance across data aggregation, new data generation, and shared data infrastructure.
Advise on end-to-end data flows, data governance, privacy, and security models.
Review technical architectures and system designs proposed by external teams for feasibility, scalability, security, and alignment with program objectives.
Advise on healthcare dataset lifecycle management and synthetic data strategies to support reproducibility and reuse in healthcare contexts.
Assess infrastructure and compute tradeoffs for scalability, interoperability, and cost.
Identify system-level risks and recommend mitigation strategies.
Serve as a technical liaison between infrastructure teams, data contributors, AI developers, clinical SMEs, and program leadership.
Develop technical documentation, architecture briefs, and presentations for internal and external stakeholders.