AI Software Developer

Booz Allen Hamilton•San Diego, CA

1d•$69,300 - $158,000•Remote

About The Position

Are you looking to bring strong hands-on technical skills, including software and integration, to cybersecurity and mission challenges affecting Navy efforts in the San Diego region? Our team is a skilled group that partners deeply with clients and mission stakeholders: we clarify the problem, shape technical approaches, and deliver practical outcomes in secure, compliant settings. This is not a typical role on a large software product team. It is a multidisciplinary environment where you may move between advisory conversations, solution and implementation planning, and focused integration and implementation, including AI-enabled automation and analytics where they reduce risk and improve speed and quality of cyber and mission workflows. You will work with cybersecurity specialists, engineers, and partners supporting Navy customers and warfare centers. Success requires curiosity, ownership, and comfort learning in classified or controlled environments. We value clear communication, sound judgment, and the ability to translate between mission language and technical reality, always with security, RMF, and operational constraints in view. With mentorship and hands-on problem-solving, we deliver outcomes that reduce cyber risk and support the fleet and ashore enterprise. Work with us to modernize Navy cyber missions through responsible use of data, automation, and AI.

Requirements

1+ years of experience in a role that includes software development, integration, or automation in a professional environment
Experience in Python for developing, testing, and debugging components others rely on services, automation, and data or integration utilities
Experience integrating APIs and working with relational, document, or NoSQL stores, and tracing failures across components
Experience delivering in small teams with Agile, Kanban, or structured collaboration, and with Git and code review norms
Experience with cloud or enterprise deployment patterns or on-prem integration environments
Ability to translate mission or security requirements into concrete technical tasks and explain tradeoffs to stakeholders
Secret clearance
Bachelor’s degree in Computer Science, Computer Engineering, Cybersecurity, Information Systems, or a STEM field
DoD 8570/8140 IAT Level II certification

Nice To Haves

Experience designing AI/ML or LLM-enabled architectures, including model selection, workflow design, orchestration, and integration into mission workflows
Experience with AI/ML or LLM-enabled patterns, such as RAG, embeddings, vector search, prompt or tool orchestration, or frameworks such as OpenAI Agents SDK, Google ADK, LangChain, or comparable
Experience with enterprise data and analytics platforms the team may leverage-examples include DoD's Advana or War Data Platform (WDP), Databricks, and Palantir Foundry
Experience implementing AI/ML or LLM solutions and model deployment locally or through cloud-based technologies
Experience conducting process transformation analyses, mapping as-is and to-be workflows, and developing solution blueprints
Experience with LLM model registries, such as Hugging Face, embedding models, and vector databases

Responsibilities

Understand the client and the problem: participate in discussions with mission and technical partners to clarify objectives, constraints, and security or compliance boundaries.
Shape solutions: contribute to technical approaches, tradeoff analysis, and implementation plans such as what to build or integrate, how to phase work, and what evidence or controls matter.
Deliver hands-on technical work: design, implement, test, and troubleshoot scripts, services, APIs, or automation, including AI/LLM-assisted workflows where appropriate, so recommendations are credible and deployable, not slide-only.
Integrate systems and data: work with REST/HTTP APIs, data stores, and enterprise or cloud patterns to connect capabilities safely and repeatably.
Document and communicate: produce clear artifacts for technical and non-technical audiences such as design notes, limitations, operational implications, or security-relevant behaviors.
Respect the cyber context: align work with RMF, control expectations, and secure engineering practices, and partner with cybersecurity and authorization stakeholders.
Improve how we work: use version control, review practices, and lightweight testing appropriate to the engagement, and stay current on responsible AI expectations in government settings.