AI/ML Engineer — Generative AI Mission Systems

AI/ML Engineer — Generative AI Mission Systems Location: Mainly remote within the United States, with onsite collaboration in Laurel, Maryland, typically one day approximately every six weeks for team-wide sprint planning. Clearance: Active final DoD Secret clearance required This position supports a pending contract opportunity and is contingent upon contract award, with an anticipated start in November 2026. Build Applied AI for Secure Mission Software Help turn generative-AI concepts into dependable capabilities used within secure mission-planning and decision-support software. At Rackner, you will integrate large language models, retrieval-augmented generation, agentic AI, prompt-engineering workflows, and inference pipelines into an established software application supporting a high-impact national-security mission. You will work across AI, software engineering, cybersecurity, DevSecOps, and customer technical teams to move capabilities beyond standalone demonstrations and into practical application workflows. This role offers the opportunity to deepen your applied-AI experience, influence how emerging capabilities are designed and evaluated, and contribute to software where reliability, security, and mission usefulness matter. This is a primarily remote role within the United States. Work will be performed using customer-provided systems, with virtual collaboration across the engineering team. Any classified work will be completed onsite at the customer location. What You’ll Do Design, develop, test, and integrate AI-enabled software capabilities. Build and integrate LLM-enabled capabilities into secure application workflows. Develop or integrate retrieval-augmented generation capabilities. Develop and support agentic-AI components and multi-step workflows. Design and refine prompts, system instructions, and supporting AI workflows. Build and maintain inference pipelines. Connect AI capabilities with existing backend services and decision-support processes. Evaluate AI outputs for grounding, reliability, accuracy, relevance, and mission usefulness. Develop tests for AI-enabled functionality and support broader integration testing. Demonstrate working prototypes and incorporate technical and user feedback. Document AI designs, workflows, limitations, evaluation results, and implementation decisions. Participate in code reviews, technical reviews, and security-remediation activities. Collaborate with software engineers, security professionals, DevSecOps teams, and customer stakeholders. What You Bring A master’s degree or Ph.D. in Artificial Intelligence, Machine Learning, Computer Science, or a related field, along with demonstrated experience working on or developing AI/ML capabilities. At least four years of relevant AI/ML experience that includes work with large language models, retrieval-augmented generation, and prompt engineering. Hands-on experience integrating LLM-enabled software and RAG capabilities into applications or workflows. Developing or supporting agentic-AI capabilities and multi-step AI workflows. Designing, building, or supporting inference pipelines. Ability to evaluate AI-enabled capabilities and clearly document findings, design decisions, and results. Testing and documenting AI-enabled software capabilities. Ability to clearly explain your personal technical ownership and contributions. Strong collaboration and technical-communication skills. Preferred Background Experience with several of the following can strengthen your fit: Moving AI capabilities beyond coursework, personal projects, or demonstrations into operational software workflows. Evaluating grounding, reliability, output quality, hallucinations, or other limitations of AI-enabled systems. Integrating AI services with backend APIs or established software applications. Secure software-development lifecycle and DevSecOps practices. OpenShift, Kubernetes, CI/CD, or containerized application delivery. Secure, restricted, disconnected, on-p…