AI Software Engineer

Requirements

• Experience with languages & frameworks such as Python, FastAPI, JavaScript, TypeScript, React
• Experience with Data & Storage including MongoDB, REST APIs, Data Processing Workflows
• Familiar with DevOps & Deployment tools such as Docker, Kubernetes, CI/CD tools, Cross-Environment Operations (HS/LS)
• Use of Automation Testing Frameworks, Git, Jira, and Confluence
• You have a passion for technology and the drive to learn new programming languages and frameworks

Education/Certification Requirements

• BS in Computer Science or similar technical field (4 years of relevant experience may be substituted in lieu of a BS degree)

Clearance Requirements

• Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information; an active TS/SCI w/FS Poly clearance is required. Please note that the FS Poly currently needs to be held by the NSA or within the past two years.

• AI Architecture Best Practices – Produce best-practices documentation and reusable implementation packages that establish repeatable patterns for building and deploying AI enabled software.
• Agentic Coding Harness Testing Framework — Build and maintain the framework that executes repeatable tests against agentic coding harnesses, consuming the datasets and scenarios produced by Fuzzy blocks and running them alongside the Data Scientists' InspectAI benchmarks to measure agent performance and reliability.
• Cross Environment Application Operations — Support deployment, configuration, and operation of applications across both HS and LS environments. This includes ongoing sustainment of deployed applications, implementing small feature requests and enhancements driven by user feedback and evolving mission needs, and diagnosing and resolving bugs across both environments.
• Fuzzy blocks – Build and maintain Fuzzy blocks, a workflow driven framework that generates vulnerability discovery datasets and experimental scenarios fed into the testing harness. It provides a structured approach to creating repeatable benchmark datasets and adversarial test scenarios for AI systems and agents, supporting both research and operational security use cases.

• Python
• FastAPI
• JavaScript / TypeScript
• React
• Docker
• Kubernetes
• Git
• CI/CD tools
• REST APIs
• MongoDB
• Cloud platforms or deployment environments
• Testing frameworks
• Collaboration tools like Jira or Confluence