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AI/ML Engineer — Generative AI Mission Systems at Rackner

Jobicy is hiring an AI/ML Engineer — Generative AI Mission Systems at Rackner for a remote role open to applicants in United States. Level rates it ; you can apply on Jobicy.

Source: Jobicy. Level lists this role from Jobicy; you apply on their page.

Level's read on this job

  • On Level's scale, this machine learning engineer role is Builds AI ●●●●: the job is building AI systems.
  • This is listed as a remote role.
  • The posting mentions prompt engineering, RAG and LLM integration among its tools and skills.
  • Jobicy has 46 other open roles on Level.
  • Posted in the last few days.

AI in this role

prompt-engineeringragllm-integration

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-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.

How we rate this

AI/ML Engineer — Generative AI Mission Systems at Rackner at Jobicy rates 84 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Prompt engineeringRAGLLM Integration

Also common in machine learning engineer jobs at this level

Pay

Typical machine learning engineer job
$197k

This posting doesn't list pay. Use the typical machine learning engineer figure when pay comes up. See machine learning engineer jobs

Questions you could be asked

  1. How do you structure and test a prompt to get consistent output from a language model?
  2. How would you design a retrieval step so the model answers from real data instead of guessing?
  3. How have you integrated a large language model into a production application?
  4. How would you decide a model or AI system is ready to ship?
  5. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: Prompt engineering, RAG, and LLM Integration. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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