Machine Learning Engineer

About the role

We are seeking a Machine Learning Engineer with a passion for building mission-critical capabilities to join our talent network. Working at NT Concepts means that you are part of an innovative, agile company dedicated to solving the most critical challenges in National Security. If meaningful work, initiative, creativity, and continuous self-improvement are important to your career, explore What's Next with us.

Mission Focus: Our machine learning teams bridge the gap between cutting-edge AI research and operational government missions. We are looking for engineers who can take machine learning and Computer Vision (CV) solutions from early research and prototyping all the way into stable, scalable production environments.

In this role, you will help design, build, and deploy automated ML workflows that directly support national security analysts and operators. We embrace modern agile practices, a DataOps/DevSecOps/MLOps ethos to "automate-first," and modern cloud-native architectures.

Clearance: Active TS/SCI required (CI Polygraph or higher preferred or must be eligible to obtain)

Location/Flexibility: Vienna, VA / Chantilly, VA (Hybrid / Flexible Remote options available)

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.

Qualifications

- Clearance: Active TS/SCI clearance.

- Hands-On Experience: Demonstrated professional experience developing, testing, and deploying machine learning models into real-world or production environments.

- Deep Learning & CV: Strong programming skills in Python and hands-on experience with deep learning frameworks (primarily PyTorch, OpenCV, TensorFlow, or NumPy).

- ML Lifecycle & MLOps: Practical familiarity with containerization (Docker, Kubernetes) and ML lifecycle/pipeline platforms (e.g., MLflow, Kubeflow, AWS SageMaker).