Jobs at Booz Allen Hamilton

Designs, implements, and optimizes secure, scalable cloud and hybrid-cloud architectures for mission-critical space sensing capabilities. Leads architecture, platform engineering, DevSecOps, and development teams; develops cloud strategies and platform roadmaps; evaluates emerging technologies; guides automation and modernization; and collaborates with technical and business stakeholders to deliver resilient enterprise systems.

Develop, test, optimize, reverse engineer, and maintain embedded software supporting hardware functionality across diverse devices and platforms. Troubleshoot operational issues, configure platform-specific solutions, collaborate with cybersecurity specialists, and create automated testing and workflow optimization solutions for mission-focused client systems.

Develop, test, deploy, and maintain deep learning computer vision models for space and missile warning applications. Build sensor fusion and target tracking solutions, optimize real-time systems with GPU programming, and contribute to embedded systems and fire control architectures using Python, C, C++, and Rust. Collaborate with multidisciplinary teams to deliver mission-critical AI capabilities.

Develops end-to-end AI and machine learning software solutions for digital engineering and systems modernization. Responsibilities include designing and implementing software, applying AI/ML models, supporting MBSE and DevSecOps capabilities, considering broader system ecosystems, and building solutions using LLM frameworks and cloud technologies. The role supports mission-critical government applications and requires eligibility for classified information access.

Design and implement AI architectures and solutions for client business needs. Develop innovative solutions using generative and agentic AI, deep learning, computer vision, NLP, and related technologies. Apply software engineering expertise, work independently, mentor team members, and potentially lead Agile teams. The role requires a bachelor’s degree, Secret clearance eligibility, and at least two years of relevant experience.

Develops, trains, tests, deploys, and maintains production computer vision and machine learning models for mission-critical applications. Responsibilities include deep learning, transformer-based vision models, sensor fusion, target tracking, GPU optimization, embedded systems, and real-time missile defense software. The engineer collaborates with multidisciplinary space, military, intelligence, engineering, and AI teams to design and implement robust operational capabilities.

Analyze structured and unstructured data for U.S. Space Force stakeholders. Responsibilities include sourcing datasets, defining KPIs, developing analytical scripts and machine learning models, automating processes, building Tableau or Power BI dashboards, and communicating insights to senior government and military leaders. The role also involves applying generative AI tools and supporting data strategy and decision-making.

Develop end-to-end AI and machine learning software solutions for mission-critical systems. Responsibilities include full-stack LLM development, RAG implementation, model selection and registry management, inference engine integration, REST API and React development, and DevSecOps CI/CD pipeline management. The role also supports digital and systems engineering modernization, considers broader operational ecosystems, and applies innovative AI technologies. Candidates must have relevant software development experience, a STEM bachelor’s degree, and the ability to obtain a Secret clearance.

Design, develop, and deploy AI/ML solutions for wired and wireless signaling data. Implement and benchmark neural networks, optimize algorithm performance, support Agile software development, collaborate cross-functionally, and mentor junior staff. Work requires eligibility for TS/SCI with polygraph and a STEM bachelor's degree.

Design, implement, and analyze signal processing and DSP software for wired and wireless signaling. Perform RF testing, develop software (Python/C/C++), follow Agile practices, collaborate with teams, and mentor junior staff while working on classified TS/SCI projects.

Design, implement, and train reinforcement learning and multi-agent algorithms; build scalable Python-based training pipelines and simulated environments; run distributed training with cloud and container orchestration; transition models to production, optimize system performance, and collaborate with domain experts to align rewards and evaluations with mission objectives.