AI Software Developer, Junior

Booz Allen Hamilton•San Diego, CA

11d•$55,300 - $126,000•Onsite

About The Position

This role is for an AI Software Developer, Junior in the San Diego region, focusing on cybersecurity and mission challenges for Navy efforts. The position involves working within a skilled team that collaborates deeply with clients and mission stakeholders to clarify problems, shape technical approaches, and deliver practical outcomes in secure, compliant settings. It's a multidisciplinary environment where the developer may engage in advisory conversations, solution and implementation planning, and focused integration and implementation, including AI-enabled automation and analytics to reduce risk and improve speed and quality of cyber and mission workflows. The developer will collaborate with cybersecurity specialists, engineers, and partners supporting Navy customers and warfare centers. Success requires curiosity, ownership, and comfort learning in classified or controlled environments. The role values clear communication, sound judgment, and the ability to translate between mission language and technical reality, with a constant focus on security, RMF, and operational constraints. With mentorship and hands-on problem-solving, the team delivers outcomes that reduce cyber risk and support the fleet and ashore enterprise, modernizing Navy cyber missions through responsible use of data, automation, and AI.

Requirements

Experience in a technical role in an academic or professional work environment
Experience integrating an API or service, such as REST, or automating a repeatable workflow using code you authored or extended
Experience using Git in a professional role or reviewed context in class, or internship
Ability to write, test, and debug code in Python
Ability to learn quickly in ambiguous problem spaces and communicate technical concepts to non-specialists
Secret clearance
Bachelor’s degree in Computer Science, Computer Engineering, Cybersecurity, Information Systems, or a STEM field
Ability to obtain DoD 8570/8140 IAT Level II certification within 30 days of hire date

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; 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.