AI​/ML Engineer — Generative AI Mission Systems

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, Dev Sec Ops , 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, Dev Sec Ops  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 Dev Sec Ops  practices.
• Open Shift, Kubernetes, CI/CD, or containerized application delivery.
• Secure, restricted, disconnected, on-premises, or classified development environments.
• Defense, government, aerospace, mission-planning, or other regulated environments.
• Collaboration with…