AI​/ML Engineer — TS​/SCI

• Computer vision

Location: Dayton, OH

Clearance: Active TS/SCI required

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.

• 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

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, Tensor Flow, 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.

Additional value may come from hands‑on work with:

• Hugging Face, Ollama, or comparable GenAI and model tooling
• APIs, microservices, or AI service…