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# AI/ML Engineer — Active TS/SCI

Canonical URL: https://nextmove.md/fde/jobs/7da975d2c49e2066

Rackner posts a FDE role to build what the customer runs in Dayton, OH with no pay posted. AI/ML Engineer building, integrating, and operationalizing machine learning, deep learning, and generative AI capabilities for a high-impact federal mission in Dayton, Ohio. Read from the employer’s posting on November 8, 2026.

- [Rackner](https://nextmove.md/fde/companies/rackner)

Posted Sep 16, 2026 by Rackner · open on Greenhouse as of Nov 8, 2026

Pay not posted Dayton, OH

AI/ML Engineer building, integrating, and operationalizing machine learning, deep learning, and generative AI capabilities for a high-impact federal mission in Dayton, Ohio.

- [See roles that fit me](https://nextmove.md/sign-up?from=%2Ffde%2Fjobs%2F7da975d2c49e2066&placement=top)
- [View posting at Rackner](https://job-boards.greenhouse.io/rackner/jobs/4734877005)

Free. Upload your resume once, see why each FDE role fits, and get introduced only when you say so.

## At a glance

### Posted base pay

Not posted

Senior FDE postings, 1,864 postings: the middle 50% of posted pay is $160K to $220K, median $190K.

### The work

Builds what the customer runs

Balance **Mostly building**

Project stages

1. Pre-sales (not covered)
2. Proof of concept
3. Implementation
4. Operations

- **Coding**: Writes production code

- **Builds for**: External customers

- **Level**: Senior

- **Role**: Individual contributor

### Do you meet what the posting states?

The posting reads as a senior role. What level are you at now?

The job is in Dayton, OH. Could you work there?

Which of these have you used in production?

Answer each one. Nothing is saved or sent.

[What postings ask for, by background →](https://nextmove.md/fde/paths)

## Conditions

- **Location**: Dayton, OH

- **Remote work restrictions**: Customer environment in Dayton, Ohio; onsite and hybrid expectations are being finalized, and candidates should be able to support work in Dayton if required. Candidates outside the area may be considered.

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## The job description from Rackner

Posted by Rackner on Sep 16, 2026

### AI/ML Engineer

(Active TS/SCI)

**Location:** Dayton, OH
**Clearance: **Active TS/SCI required

### Build AI That Moves From Model to Mission

Rackner is seeking an AI/ML Engineer to build, integrate, and operationalize artificial intelligence and machine learning capabilities supporting a high-impact federal mission.

In this role, you will work across the AI lifecycle — from model development and evaluation through software integration, AI services, and user enablement. You will help turn emerging AI capabilities into practical tools that analysts, developers, and mission teams can use.

This is a strong fit for an engineer who wants broader ownership than training models in isolation. You will have opportunities to shape technical approaches, work directly with the people using the capability, and help determine how AI moves from experimentation into secure, scalable mission use.

The work supports a customer environment in Dayton, Ohio. Onsite and hybrid expectations are still being finalized, so candidates should be prepared to discuss their ability to support work in Dayton if required. Strong qualified candidates outside the area may also be considered while those requirements are being finalized.

Because this work supports a federal customer, an active TS/SCI clearance is required.

### What You'll Do

- Build and evaluate machine learning, deep learning, and generative AI solutions for real-world mission use cases
- Develop and integrate AI/ML capabilities across software applications, APIs, services, data pipelines, and existing technical environments
- Assess models and data to determine suitability, availability, access requirements, technical feasibility, and potential mission value
- Enable LLM and GenAI capabilities through repeatable workflows, interfaces, services, and approved access mechanisms
- Prepare and curate datasets for model training, evaluation, testing, and operational AI workflows
- Translate prototypes into usable capability by addressing performance, scalability, reproducibility, integration, and deployment
- Develop interfaces and services that allow applications and users to interact with AI/ML capabilities and associated model or provider metadata
- Partner with software engineers, data engineers, technical leadership, product stakeholders, and subject-matter experts
- Create technical documentation, implementation guidance, runbooks, and repeatable processes that support adoption and sustainment
- Evaluate deployed capabilities and use technical and user feedback to improve performance and mission utility

### What We're Looking For

You bring a strong hands-on foundation in applied machine learning or AI engineering and can demonstrate work that progressed beyond theoretical exercises or isolated notebooks.

You should be comfortable developing software — preferably with Python — and working with a major ML framework such as PyTorch, TensorFlow, or comparable technologies.

Your technical background may be strongest in areas such as:

- Computer vision
- Natural language processing
- Generative AI and Large Language Models
- Predictive modeling
- Object detection or classification
- Modeling and simulation
- Other applied machine learning domains

You do not need to be an expert in every area.

Strong candidates will be able to clearly explain what they built, what they personally owned, why they made particular technical decisions, how they evaluated the result, and how the capability ultimately reached its users.

You should also be comfortable working with datasets used for model development and evaluation and collaborating across software, data, systems, and mission teams.

### Preferred Background

Additional value may come from hands-on work with:

- Hugging Face, Ollama, or comparable GenAI and model tooling
- APIs, microservices, or AI service integration
- Docker, containers, or cloud-native deployment
- AWS, Azure, or similar cloud environments
- DevSecOps or MLOps practices
- Model or data versioning and reproducible AI workflows
- Metadata management and model/provider catalogs
- Production AI/ML pipelines
- Large-scale, sensitive, or regulated datasets
- Federal, DoD, Intelligence Community, or similarly controlled technical environments
- Direct collaboration with mission users, product owners, technical leadership, or government stakeholders

These are advantages, not a checklist. We are primarily looking for engineers who can connect strong AI/ML fundamentals with sound software engineering and practical implementation.

### About Rackner

Rackner is a software consultancy focused on building mission-critical systems for the U.S. government.

Our teams work across cloud platforms, DevSecOps, AI/ML, data systems, and modern software engineering initiatives supporting federal agencies and national security missions.

Rackner engineers and technical teams collaborate closely with leadership, program teams, and mission stakeholders to design, deliver, and improve systems that address complex operational challenges.

### Benefits & Perks

At Rackner, we believe that when our people grow, our company grows with them. We support continued learning, professional development, and meaningful opportunities to contribute across mission-focused programs.

Benefits include company-supported certifications aligned with current and future program work; advancement and leadership opportunities; a 401(k) with 100% match up to 6%; comprehensive medical, dental, vision, life, and disability coverage; generous PTO and paid holidays; and home-office equipment and remote-work support, where applicable.

### Equal Opportunity

Rackner is an equal opportunity employer and considers all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or other protected characteristics.

[View posting at Rackner](https://job-boards.greenhouse.io/rackner/jobs/4734877005)

## About Rackner

Cloud-native software consultancy building AI/ML, DevSecOps, and Zero Trust platforms for US federal and defense agencies, plus enterprise and startup clients.

Bootstrapped · 10–50 people

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