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Rackner

DevSecOps and AI from Cloud to Mission Edge | Kubernetes Partner | Multicloud | 8(a) | HUBZone

AI/ML Engineer TS/SCI

AI EngineerMachine Learning EngineerFull TimeRemoteMid LevelTeam 11-50H1B No SponsorCompany SiteLinkedIn

Location

United States

Posted

69 days ago

Salary

0

Seniority

Mid Level

Job Description

AI/ML Engineer TS/SCI

Rackner

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 - Understanding of MLOps principles and pipelines Benefits & Perks - Weekly pay with full remote flexibility - Professional growth investment, including paid certifications and training - Comprehensive benefits package, including: - Medical, dental, and vision coverage - 401(k) with 100% company match up to 6% - Paid time off (PTO) - Life and disability insurance - Home office equipment plan - A supportive, inclusive team culture focused on collaboration, trust, and mission impact About Rackner Rackner is a cloud-native software consultancy delivering solutions for startups, enterprises, and the public sector. We enable digital transformation through DevSecOps, AI/ML, and cloud-first innovation. Our teams solve high-impact problems that advance federal missions and strengthen national readiness. Join us to help shape the future of secure, scalable data systems supporting mission success.

Job Requirements

  • 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
  • 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

Benefits

  • Weekly pay with full remote flexibility
  • Professional growth investment, including paid certifications and training
  • Comprehensive benefits package, including:
  • Medical, dental, and vision coverage
  • 401(k) with 100% company match up to 6%
  • Paid time off (PTO)
  • Life and disability insurance
  • Home office equipment plan
  • A supportive, inclusive team culture focused on collaboration, trust, and mission impact

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