Rackner
cloud-native software consultancy
AI/MLEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“AI/ML Engineer at Rackner. Skills: AI/ML, machine learning, deep learning, model architectures, LLMs, object detection, PyTorch, TensorFlow, Hugging Face, Ollama, data engineering, DevSecOps, MLOps. Design, develop, and implement machine learning and deep learning models. Build and optimize model architectures”
What You'll Achieve.
deliver impactful AI-driven solutions; advance federal missions; strengthen national readiness; help shape the future of secure, scalable data systems supporting mission success
Industry & Context.
problem-solving skills
Active TS/SCI, TS/SCI
What They're Looking For.
Must Have
designing and implementing model architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer-based architectures, Large Language Models (LLMs), Object Detection models, PyTorch, TensorFlow, Hugging Face, Ollama, data engineering concepts, Feature engineering, dataset preparation, Data versioning tools, lakeFS, Metadata standards, STAC, AI/ML training runbooks, problem-solving skills, ability to work in a collaborative environment
Nice to Have
deploying models in cloud-native environments, DevSecOps practices, working with large-scale or federal datasets, MLOps principles and pipelines
What You'll Do.
and implement machine learning and deep learning models
Build and optimize model architectures
Develop and deploy Large Language Models (LLMs) and object detection systems
Perform feature engineering and prepare high-quality datasets for training and evaluation
Create and maintain AI/ML training runbooks and documentation
Ensure reproducibility through data versioning and metadata standards
Continuously evaluate and improve model performance and scalability
How You'll Work.
Team & Collaboration
collaborating with cross-functional teams; Collaborate with data engineers and software teams to integrate models into production systems; ability to work in a collaborative environment; supportive, inclusive team culture focused on collaboration
Communication Scope
create clear and effective AI/ML training runbooks
Full Job Description
Job Title: AI/ML Engineer Location: Dayton, OH and Remote 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 environments Familiarity with DevSecOps practices Experience working with large-scale or federal datasets Un
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