AI/ML Software Engineer

Responsibilities

• Design and implement APIs, data pipelines, and simulation runtime logic that connect and enable mission applications.
• Develop software using modern programming languages such as Java, Python, C++, or TypeScript/Angular.
• Write clean, testable, and maintainable code following secure coding and software engineering best practices.
• Build and integrate modular microservices to improve scalability, maintainability, and interoperability.
• Cloud & Containerized Environments
• Build and deploy containerized, cloud-native services using Docker, Kubernetes, and CI/CD pipelines (GitLab, Jenkins, or equivalent).
• Implement Infrastructure-as-Code and automation scripts to accelerate deployment and configuration management.
• Contribute to secure deployments across hybrid or disconnected environments (IL4–IL6, AWS GovCloud, or on-prem).
• Systems Integration & Distributed Computing
• Develop distributed systems and data integration frameworks using message buses such as Kafka or Redis.
• Engineer data flow between analytic, AI, and simulation components to support real-time mission use cases.
• Collaborate with system engineers and architects to ensure interoperability across software ecosystems.
• Data & Analytics Integration
• Build and manage databases (PostgreSQL, MongoDB, graph DBs) and model complex data relationships.
• Develop data services that feed analytics pipelines or integrate AI/ML outputs into runtime systems.
• Work with serialization and exchange formats such as JSON, Protobuf, GeoJSON, or KML.
• Security, Testing & Sustainment
• Write, test, and deploy software within secure or classified environments.
• Automate testing and monitoring to ensure performance, reliability, and repeatable deployments.
• Support the transition of prototypes to operational systems, focusing on maintainability and observability.

Qualifications

• Minimum Requirements: Must be a U.S. citizen and be willing to obtain and maintain a security clearance, as needed. 6-10+ years of professional software engineering experience. 3+ years of professional experience with DevSecOps, Zero-Trust, or ATO/RMF processes in Department of Defense (DoD/DoW) environments. Strong full-stack or systems engineering background. Proficiency in one or more of the following languages: Java, Python, C++, or TypeScript/Angular. Experience building containerized, cloud-native solutions using Docker, Kubernetes, and CI/CD pipelines. Complete understanding of distributed systems and message buses (Kafka, Redis, etc.). Experience developing or integrating analytics and AI models into production systems.
• Preferred Qualifications: Experience deploying code in IL4–IL6 or edge/disconnected environments. Familiarity with databases such as PostgreSQL, MongoDB, or graph databases. Knowledge of Infrastructure-as-Code (Terraform, CloudFormation, or CDK). Bachelor’s degree in Computer Science, Software Engineering, or a related technical field. Active Secret clearance preferred; ability to obtain one is required.