AI Software Engineer, Senior

Booz Allen Hamilton•Honolulu, HI

1d•$86,900 - $198,000

About The Position

As an experienced software engineer, you know how to design, develop, and deliver production AI applications that demonstrate the practical value of generative AI, large language models (LLMs), and autonomous workflows. You combine strong software engineering fundamentals with modern AI development practices to build reliable, scalable, and secure systems that solve real-world problems. In this role, you'll design AI applications that leverage prompting, retrieval-augmented generation (RAG), agentic workflows, evaluation pipelines, and human-in-the-loop interactions to deliver measurable mission impact. You'll build modular, reusable AI capabilities that integrate multiple model providers and external tools while optimizing for performance, cost, observability, and safety. You'll rapidly prototype and iterate using AI-assisted development tools, applying eval-driven development and continuous experimentation to validate solutions before deploying them into production. Working alongside data engineers, data scientists, solution architects, and product owners, you'll help define the architecture and engineering practices behind mission-critical AI applications while collaborating with a multidisciplinary team to deliver impactful mission solutions.

Requirements

3+ years of experience with software engineering in a professional work environment
2+ years of experience developing AI or machine learning solutions in a professional environment
Experience building production AI applications using programming languages such as Python
Experience designing and implementing generative AI applications using large language models
Experience building retrieval-augmented generation (RAG) solutions and integrating AI systems with enterprise data
Experience with AI orchestration frameworks such as LangChain
Experience developing AI applications that interact with external tools, APIs, or enterprise systems, and evaluating and improving AI application quality through testing, experimentation, or benchmarking
Experience deploying cloud-native applications using cloud platforms such as AWS
Secret clearance
Bachelor's degree in Computer Science, Engineering, or a technology field

Nice To Haves

Experience building AI applications that incorporate autonomous or agentic workflows
Experience with multimodal AI applications involving text, images, audio, or documents
Experience designing AI evaluation, observability, or safety frameworks
Experience deploying portable, edge, or offline-capable AI applications
Experience developing user interfaces that enable effective interaction with AI applications
Experience delivering technical solutions in client-facing environments
Experience using AI-assisted software development tools to improve engineering productivity
Master's degree in Computer Science, AI, or a related technical field

Responsibilities

Design and develop production AI applications that integrate foundation models, retrieval systems, external tools, and enterprise data sources.
Architect modular and reusable AI components for prompting, retrieval, orchestration, tool execution, memory, and evaluation.
Build AI applications that leverage structured outputs, embeddings, RAG, long-context reasoning, and agentic workflows where appropriate.
Develop data pipelines for ingesting, transforming, indexing, and refreshing structured and unstructured data used by AI applications.
Implement evaluation frameworks to measure correctness, robustness, safety, and mission outcomes using both automated and human evaluation techniques.
Optimize AI applications for latency, reliability, scalability, and operational cost through experimentation, benchmarking, and continuous improvement.
Apply asynchronous programming and event-driven design patterns to support long-running workflows and distributed AI applications.
Incorporate observability, monitoring, guardrails, access controls, and responsible AI practices throughout the application lifecycle.
Deploy AI services securely on AWS or equivalent cloud platforms using Docker, Kubernetes, serverless, or other cloud-native deployment patterns.
Apply modern software engineering practices, including automated testing, CI/CD, prompt and workflow versioning, and safe rollout of AI updates.
Leverage AI-assisted development tools to accelerate implementation while maintaining engineering rigor, maintainability, and code quality.
Collaborate with clients and cross-functional teams to identify high-value AI opportunities, rapidly prototype solutions, and transition successful concepts into production.
Present technical solutions to both technical and non-technical stakeholders.