AI & Decision Engineer

Description

The Systems Performance Analysis Group (KBS) at the Johns Hopkins University Applied Physics Laboratory is seeking a Generative AI & Decision Engineer to design, build, and deploy AI-enabled analytical and decision-support applications for Naval and Air Force sponsors. You will lead the development of human–LLM interaction capabilities, Retrieval-Augmented Generation (RAG) systems, and intelligent agents that operate at scale to support a variety of applications. This role will collaborate closely with internal stakeholders and external sponsors, including DoD organizations, to demonstrate and transition cutting-edge AI technologies into operationally relevant environments.

As an AI & Decision Engineer, you will...

Architect, develop, and evolve a Generative AI-powered analytical applications that support human–LLM interactions and LLM-driven decision-making at scale.

• Design and implement LLM agents that can autonomously plan, reason, and act using tools such as RAG, analytical tools, and relevant databases.
• Apply prompt engineering techniques with state-of-the-art models to produce data-driven recommendations based operational and developmental data for a variety of systems.
• Design, implement, and manage RAG pipelines using vector databases and frameworks to integrate relevant documentation, prior analytical results, and large information repositories into LLM decision-making.
• Develop and maintain agents capable of: (1) Interpreting database schemas and generating SQL queries to answer user questions (SQL agents). (2) Translating natural language inputs into structured actions and game events stored in a database for downstream simulation and adjudication.
• Experiment with and evaluate prompting strategies to improve reasoning quality, robustness, and transparency of model outputs in high-consequence decision contexts.
• Integrate orchestration and observability tools (e.g., Prefect) to monitor LLM pipelines, track outputs, and provide real-time insight into system behavior during wargame execution.
• Fine-tune and adapt foundation models (e.g., Llama, Mistral) using AWS SageMaker, Hugging Face TRL, and synthetic data generation (e.g., GPT-4 series) to optimize performance on sponsor-specific tasks such as deductive coding, domain knowledge transfer, etc.
• Engage with internal leadership and external sponsors to demonstrate capabilities, collect requirements, and potentially transition systems to operational users.
• Document system architectures, experiments, evaluation results, and operational guidance; prepare and present technical briefings and reports to technical and non-technical stakeholders.
• Collaborate in multidisciplinary teams (AI/ML, software, human factors, operations analysts) to integrate AI capabilities into broader analytic and operational workflows.

Qualifications

You meet our minimum qualifications for the job if you have...

• Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, Data Science, or a closely related field.
• Experience building production or prototype applications involving large language models (LLMs) or other generative models.
• Proficiency in Python and at least one modern web/backend framework (e.g., FastAPI, Flask, Django).
• Experience with relational databases, including schema design and query development (e.g., PostgreSQL).
• Hands-on experience with at least one of:
  • Retrieval-Augmented Generation (RAG) systems.
  • LLM agent frameworks (tool use, ReAct, chain-of-thought–style prompting).
  • Vector databases (e.g., Qdrant, ChromaDB).
• Demonstrated ability to comprehend and synthesize complex technical or scientific information and make timely, well-reasoned decisions
• Strong written and oral communication skills, including experience preparing technical analyses and presenting findings to a range of audiences.
• Hold an active Secret security clearance and can ultimately obtain Top Secret level clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship.

You’ll go above and beyond our minimum requirements if you have...

• Advanced degree (M.S. or Ph.D.) in Computer Science, AI/ML, Applied Mathematics, or related field.
• Experience with:
  • LLM frameworks and libraries (e.g., Hugging Face Transformers, TRL, LangChain).
  • Fine-tuning and evaluating open-weight models (e.g., Llama, Mistral) on domain-specific tasks.
  • Cloud platforms and MLOps tooling (e.g., AWS, SageMaker, Prefect, MLflow).
  • Frontend development using React and integration with RESTful backends (e.g., FastAPI).
  • Experience developing AI systems in defense, space, or government contexts, including familiarity with military doctrine, wargaming, operational analysis, or mission planning.
  • Prior work on applications supporting Navy, DARPA, or other DoD sponsors, particularly in knowledge-management or decision-support contexts.
  • Demonstrated ability to engage with sponsors, understand mission needs, negotiate requirements, and translate them into technical solutions.

Additional Information

• This position may require occasional travel to sponsor sites and test events.
• Candidates will be expected to work in multidisciplinary teams and contribute to both research and applied development efforts.
• APL is committed to fostering an inclusive environment and encourages applications from diverse backgrounds.

About Us

Why Work at APL?

The Johns Hopkins University Applied Physics Laboratory (APL) brings world-class expertise to our nation’s most critical defense, security, space and science challenges. While we are dedicated to solving complex challenges and pioneering new technologies, what makes us truly outstanding is our culture. We offer a vibrant, welcoming atmosphere where you can bring your authentic self to work, continue to grow, and build strong connections with inspiring teammates.

At APL, we celebrate our differences of perspectives and encourage creativity and bold, new ideas. Our employees enjoy generous benefits, including a robust education assistance program, unparalleled retirement contributions, and a healthy work/life balance. APL’s campus is located in the Baltimore-Washington metro area. Learn more about our career opportunities at

All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, genetic information, veteran status, occupation, marital or familial status, political opinion, personal appearance, or any other characteristic protected by applicable law. APL is committed to providing reasonable accommodation to individuals of all abilities, including those with disabilities. If you require a reasonable accommodation to participate in any part of the hiring process, please contact View email address on click.appcast.io.

The referenced pay range is based on JHU APL’s good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level with consideration for internal parity. For salaried employees scheduled to work less than 40 hours per week, annual salary will be prorated based on the number of hours worked. APL may offer bonuses or other forms of compensation per internal policy and/or contractual designation. Additional compensation may be provided in the form of a sign-on bonus, relocation benefits, locality allowance or discretionary payments for exceptional performance. APL provides eligible staff with a comprehensive benefits package including retirement plans, paid time off, medical, dental, vision, life insurance, short-term disability, long-term disability, flexible spending accounts, education assistance, and training and development. Applications are accepted on a rolling basis.

Minimum Rate

$100,000 Annually

Maximum Rate

$245,000 Annually