[All remote jobs](https://jobicy.com/jobs.md)[![Rackner logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2026/11/05f1f6baeea0-221.webp)](https://jobicy.com/company/rackner.md)[Rackner](https://jobicy.com/company/rackner.md) # AI/ML Engineer — Generative AI Mission Systems AI Summary This role builds and integrates generative AI capabilities for secure mission-planning and decision-support software. The engineer will work with LLMs, retrieval-augmented generation, agentic workflows, prompt engineering, inference pipelines, backend services, and AI evaluation. The position requires an active final DoD Secret clearance, advanced education, and at least four years of relevant AI/ML experience. It is primarily remote within the United States but requires periodic onsite collaboration in Laurel, Maryland and onsite work for classified activities. Success depends on delivering reliable, secure, well-tested AI features in collaboration with software, cybersecurity, DevSecOps, and customer teams.[Apply for this job](#job-application)[View company](https://jobicy.com/company/rackner.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUndisclosedDepartment[Software Engineering](https://jobicy.com/categories/engineering.md)EmploymentFull TimeExperienceOpen levelPublished13 Nov 2026Apply before13 Dec 2026Listing views42Application actions3Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. Opportunity details ## About this role. 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](https://jobicy.com/company/rackner.md), 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-premises, or classified development environments. * Defense, government, aerospace, mission-planning, or other regulated environments. * Collaboration with software-engineering, cybersecurity, platform, and customer-facing technical teams. Why Rackner At Rackner, you will have the opportunity to build technology that supports critical defense and public-sector missions. You will work on more than isolated AI experiments or prompt-engineering tasks. This role combines hands-on LLM integration, retrieval and agentic-AI development, secure software delivery, and close collaboration across AI, software, cybersecurity, DevSecOps, and mission-focused teams. Rackner has delivered more than $30 million in recent federal awards and supports mission-critical work across defense, civilian, and public-sector environments. We are looking for an applied AI engineer who can build on that momentum by turning emerging generative-AI capabilities into secure, dependable software with meaningful mission impact. Benefits & Professional Growth * Competitive compensation * Company-supported certifications aligned with current and future program work * 401(k) with 100% company match up to 6% * Medical, dental, vision, life, and disability coverage * Paid time off and company holidays * Remote-work support and home-office equipment plan * Fitness and wellness reimbursement * Weekly pay schedule * Professional-development and future growth opportunities Apply If you are an AI/ML engineer who wants to move beyond standalone prototypes and help integrate LLM, RAG, and agentic-AI capabilities into secure mission software, we would like to hear from you. Show more [Apply now >](https://jobicy.com/jobs/161917-ai-ml-engineer-generative-ai-mission-systems.md) > Annual salary information is not provided for this position. Explore salary ranges for similar roles in our [Salary Directory ›](https://jobicy.com/salaries.md) * ![Upload CV](data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSI2NSIgaGVpZ2h0PSI2NSIgZmlsbD0ibm9uZSIgeG1sbnM6dj0iaHR0cHM6Ly92ZWN0YS5pby9uYW5vIj48ZyBjbGlwLXBhdGg9InVybCgjQSkiPjxwYXRoIGQ9Ik0wIDBINjVWNjVIMFYwWiIgZmlsbD0iIzAyOWFlYiIvPjxnIGZpbGw9IiNmZmYiIHN0cm9rZT0iI2ZmZiIgc3Ryb2tlLXdpZHRoPSIyIj48cGF0aCBkPSJNMzMuMDQ5IDE1LjQ1NGExLjQzIDEuNDMgMCAwIDAtMi4wOTcgMGwtNy41NzkgOC4xNDdhMS4zOCAxLjM4IDAgMCAwIC4wOSAxLjk3MyAxLjQ0IDEuNDQgMCAwIDAgMi4wMDgtLjA4OGw1LjEwOS01LjQ5MnYyMC42MWExLjQxIDEuNDEgMCAwIDAgMS40MjEgMS4zOTdjLjc4NSAwIDEuNDIxLS42MjUgMS40MjEtMS4zOTd2LTIwLjYxbDUuMTA5IDUuNDkyYTEuNDQgMS40NCAwIDAgMCAyLjAwOC4wODggMS4zOCAxLjM4IDAgMCAwIC4wOS0xLjk3M2wtNy41NzktOC4xNDZ6TTE2Ljc2OSAzOC40YzAtLjc3My0uNjItMS40LTEuMzg1LTEuNFMxNCAzNy42MjcgMTQgMzguNHYuMTAybC4yMTUgNi4yMjljLjIyMyAxLjY4LjcwMSAzLjA5NSAxLjgxMyA0LjIxOHMyLjUxIDEuNjA3IDQuMTcyIDEuODMzYzEuNi4yMTggMy42MzYuMjE4IDYuMTYuMjE4aDExLjI4bDYuMTYtLjIxOGMxLjY2Mi0uMjI2IDMuMDYxLS43MDkgNC4xNzItMS44MzNzMS41ODktMi41MzggMS44MTMtNC4yMThDNTAgNDMuMTEzIDUwIDQxLjA1NSA1MCAzOC41MDNWMzguNGMwLS43NzMtLjYyLTEuNC0xLjM4NS0xLjRzLTEuMzg1LjYyNy0xLjM4NSAxLjRsLS4xOSA1Ljk1OGMtLjE4MiAxLjM3LS41MTUgMi4wOTUtMS4wMjYgMi42MTJzLTEuMjI4Ljg1My0yLjU4MyAxLjAzOGMtMS4zOTUuMTktMy4yNDMuMTkzLTUuODkzLjE5M0gyNi40NjJjLTIuNjUgMC00LjQ5OC0uMDAzLTUuODkzLS4xOTMtMS4zNTUtLjE4NC0yLjA3Mi0uNTIxLTIuNTgzLTEuMDM4cy0uODQ0LTEuMjQyLTEuMDI2LTIuNjEyYy0uMTg3LTEuNDEtLjE5MS0zLjI3OS0uMTkxLTUuOTU4eiIvPjwvZz48L2c+PGRlZnM+PGNsaXBQYXRoIGlkPSJBIj48cGF0aCBmaWxsPSIjZmZmIiBkPSJNMCAwaDY1djY1SDB6Ii8+PC9jbGlwUGF0aD48L2RlZnM+PC9zdmc+) ### Upload your resume now To unlock remote work opportunities and be discovered by global employers. This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 4/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThe role combines advanced applied generative AI engineering with secure, regulated mission software delivery and clearance requirements. Candidates must translate experimental AI techniques into dependable, evaluated production workflows while coordinating across several technical stakeholders. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Highly competitive$180,000US market range (per year)$155k–$205k0$225.50k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer base-pay range is disclosed. The yearly cadence and USD market estimate are inferred from the senior, full-time US AI/ML engineering scope, required active DoD Secret clearance, advanced degree, and secure mission-software responsibilities. ## Core skills Skills and capabilities most closely associated with this opportunity. [Generative AI](https://jobicy.com/jobs?search_keywords=Generative%20AI.md)[Large Language Models](https://jobicy.com/jobs?search_keywords=Large%20Language%20Models.md)[Retrieval-Augmented Generation](https://jobicy.com/jobs?search_keywords=Retrieval-Augmented%20Generation.md)[Agentic AI](https://jobicy.com/jobs?search_keywords=Agentic%20AI.md)[Prompt Engineering](https://jobicy.com/jobs?search_keywords=Prompt%20Engineering.md)[Inference Pipelines](https://jobicy.com/jobs?search_keywords=Inference%20Pipelines.md)[Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[Backend Integration](https://jobicy.com/jobs?search_keywords=Backend%20Integration.md)[AI Evaluation](https://jobicy.com/jobs?search_keywords=AI%20Evaluation.md)[Software Testing](https://jobicy.com/jobs?search_keywords=Software%20Testing.md)[DevSecOps](https://jobicy.com/jobs?search_keywords=DevSecOps.md)[Kubernetes](https://jobicy.com/jobs?search_keywords=Kubernetes.md)[OpenShift](https://jobicy.com/jobs?search_keywords=OpenShift.md)[Secure Software Development](https://jobicy.com/jobs?search_keywords=Secure%20Software%20Development.md) Sample interview questionsHow would you evaluate whether a RAG system is sufficiently grounded for a mission-critical workflow?I would define task-specific acceptance criteria, build a representative evaluation set with verified source material, and measure retrieval quality, citation accuracy, answer correctness, completeness, and abstention behavior. I would also test adversarial and edge cases, review failure patterns with domain experts, and establish monitoring and regression testing before release. Describe an approach for integrating an LLM capability into an existing secure backend application. I would begin with the application boundaries, data classification rules, and user workflow. I would expose the capability through a controlled service interface, implement authentication, authorization, audit logging, input and output controls, and connect it to approved retrieval sources. I would then add automated tests, observability, evaluation gates, and a staged deployment plan appropriate for the environment. What are key risks in agentic AI workflows, and how would you mitigate them? Key risks include uncontrolled tool use, prompt injection, untrusted retrieval content, excessive permissions, unreliable multi-step execution, and poor traceability. I would use least-privilege tool access, explicit allowlists, validation at each step, bounded execution budgets, human approval for high-impact actions, structured logs, and deterministic fallback paths. How do you design an inference pipeline for a restricted or disconnected environment? I would select deployable approved models and dependencies, package artifacts reproducibly, and ensure model serving can operate without external network calls. The design would include resource sizing, container security, versioned model and prompt assets, offline observability, access controls, and a documented process for updates, vulnerability remediation, and validation. Tell us how you communicate AI limitations to non-AI technical stakeholders. I translate limitations into concrete workflow impact, such as when the system may produce unsupported answers, miss relevant context, or require user verification. 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