Machine Learning Engineer

Summary

NT Concepts is an innovative company focused on solving critical national security challenges. The Machine Learning Engineer will develop and deploy computer vision and machine learning solutions from research prototypes into scalable production environments, building automated ML workflows and integrating models into secure, cloud-native systems.

Responsibilities

• **Prototype to Production:** Support the full machine learning lifecycle, taking computer vision models from experimentation and notebooks into containerized, high-throughput production microservices
• **Mission Alignment:** Work closely with mission partners, domain experts, and technical teams to understand real-world operational challenges and translate them into practical ML requirements
• **MLOps & Pipeline Automation**: Build, maintain, and optimize robust pipelines for data preparation, model training, validation, versioning, deployment, and monitoring using modern tools (such as MLflow, Kubeflow, and GitLab CI/CD)
• **Model Development & Tuning:** Train, fine-tune, and evaluate deep learning algorithms for computer vision tasks (e.g., object detection, classification, segmentation, tracking)
• **System Integration:** Collaborate with cross-functional software engineers and cloud architects to integrate ML models cleanly into larger enterprise systems and secure cloud infrastructures
• **Optimization & Governance:** Optimize inference performance, apply secure coding practices, and monitor models for drift and reliability once deployed

Skills

• Active TS/SCI clearance
• Demonstrated professional experience developing, testing, and deploying machine learning models into real-world or production environments
• Strong programming skills in Python and hands-on experience with deep learning frameworks (primarily PyTorch, OpenCV, TensorFlow, or NumPy)
• Practical familiarity with containerization (Docker, Kubernetes) and ML lifecycle/pipeline platforms (e.g., MLflow, Kubeflow, AWS SageMaker)
• Familiarity working in cloud environments (AWS, Azure, or GCP) and modern development practices (Git, CI/CD pipelines, Agile methodologies)
• Ability to understand the end-user's mission objectives, iterate based on user feedback, and clearly communicate technical approaches
• CI Polygraph or higher preferred
• Experience working within secure, air-gapped, or classified cloud environments (e.g., AWS GovCloud / C2S)
• Experience with synthetic data generation techniques or multi-modal models
• Exposure to Large Language Models (LLMs) or generative AI workflows
• Familiarity with distributed model training and GPU resource management

Qualifications

Must Haves

• Active TS/SCI clearance
• Demonstrated professional experience developing, testing, and deploying machine learning models into real-world or production environments
• Strong programming skills in Python and hands-on experience with deep learning frameworks (primarily PyTorch, OpenCV, TensorFlow, or NumPy)
• Practical familiarity with containerization (Docker, Kubernetes) and ML lifecycle/pipeline platforms (e.g., MLflow, Kubeflow, AWS SageMaker)
• Familiarity working in cloud environments (AWS, Azure, or GCP) and modern development practices (Git, CI/CD pipelines, Agile methodologies)
• Ability to understand the end-user's mission objectives, iterate based on user feedback, and clearly communicate technical approaches

Nice to Haves

• CI Polygraph or higher preferred
• Experience working within secure, air-gapped, or classified cloud environments (e.g., AWS GovCloud / C2S)
• Experience with synthetic data generation techniques or multi-modal models
• Exposure to Large Language Models (LLMs) or generative AI workflows
• Familiarity with distributed model training and GPU resource management

Benefits

• Hybrid / Flexible Remote options available
• Competitive benefits
• Opportunities to bolster skills and develop new abilities
• Strong professional growth opportunities