AI/ML Engineer (Active TS/SCI )
Company: Rackner
Location: Dayton, OH (Remote)
Type: Full-time
Remote: Yes
Posted: 2026-07-09
About this role
Job Title: AI/ML Engineer
Location:
Dayton, OH
Employment Type:
Full-Time
Clearance requirements:
TS/SCI
About The Role
Rackner is seeking a highly skilled
AI/ML Engineer
to design, develop, and deploy advanced machine learning solutions that support mission-critical systems. This role will focus on building scalable models, developing training pipelines, and collaborating with cross-functional teams to deliver impactful AI-driven solutions.
Key Responsibilities
- Design, develop, and implement machine learning and deep learning models
- Build and optimize model architectures including CNNs, RNNs, and transformer-based models
- Develop and deploy Large Language Models (LLMs) and object detection systems (e.g., YOLO, Faster R-CNN)
- Perform feature engineering and prepare high-quality datasets for training and evaluation
- Create and maintain AI/ML training runbooks and documentation
- Collaborate with data engineers and software teams to integrate models into production systems
- Ensure reproducibility through data versioning and metadata standards
- Continuously evaluate and improve model performance and scalability
Required Qualifications
- Strong proficiency in designing and implementing model architectures, including:
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Transformer-based architectures
- Large Language Models (LLMs)
- Object Detection models (e.g., YOLO, Faster R-CNN)
- Hands-on experience with:
- PyTorch and/or TensorFlow
- Hugging Face, Ollama, or similar frameworks
- Experience with data engineering concepts, including:
- Feature engineering and dataset preparation
- Data versioning tools (e.g., lakeFS)
- Metadata standards such as STAC
- Ability to create clear and effective AI/ML training runbooks
- Strong problem-solving skills and ability to work in a collaborative environment
Preferred Qualifications
- Experience deploying models in cloud-native ...