AI/ML Engineer, Mid (Clearance Required)

Noblis•Reston, VA

About The Position

Noblis is seeking an experienced AI/ML Engineer to support mission-critical national security initiatives. In this role, you will design, develop, and deploy advanced machine learning solutions while building the infrastructure required to operationalize AI capabilities in secure, production environments. Noblis and its wholly owned subsidiaries, Noblis ESI and Noblis MSD, tackle some of the nation's toughest challenges, delivering advanced solutions to customers' most critical missions. We combine leading scientific, engineering, and management expertise in a culture of objectivity and collaboration, ensuring our work has a lasting impact across federal missions. We collaborate with a wide range of government agencies in the defense, intelligence, and federal civilian sectors. At Noblis, we share a passion for excellence and innovation, and we foster an environment where people can perform meaningful work while maintaining a balance that keeps them energized and fulfilled. We seek individuals with natural curiosity and a desire to collaborate and learn. We believe our people are our greatest asset, and we consistently seek exceptionally skilled, mission-driven professionals who care deeply about doing work that enriches lives and makes our nation safer. Noblis has received numerous workplace awards for its culture, commitment to employee well-being, and dedication to meaningful, impactful work. We maintain a drug-free workplace. Remote/hybrid status is subject to change based on Noblis and/or government requirements.

Requirements

Active Top Secret/SCI (TS/SCI) clearance with a current Polygraph.
Bachelor’s degree with 5 years of related experience; OR Master's degree with 3 years of related experience; OR associate’s degree with 8 years of related experience; OR High School diploma/GED with 11 years of related experience.
Experience deploying machine learning (ML) models to production, including large language models (LLMs)
Strong proficiency with machine learning (ML) frameworks and containerization technologies (e.g., PyTorch, Docker, and Kubernetes)
Full-stack software development experience using Python and JavaScript
Working knowledge of AWS cloud services and infrastructure
Demonstrated experience implementing MLOps and DevOps best practices, including CI/CD, model deployment, monitoring, and automation
U.S. Citizenship is required

Nice To Haves

Expert-level proficiency in Python with extensive experience across leading machine learning (ML) frameworks, including TensorFlow, PyTorch, and scikit-learn
Proven ability to design and implement end-to-end machine learning (ML) pipelines, spanning data ingestion, feature engineering, model training, evaluation, deployment, and monitoring
Extensive experience with large language models (LLMs), including fine-tuning, prompt engineering, retrieval-augmented generation (RAG), agentic workflows, and responsible AI practices
Expertise in advanced machine learning (ML) techniques, including deep learning, reinforcement learning, generative models, ensemble methods, and modern model optimization approaches
Proven track record of designing and implementing production-grade MLOps infrastructure, including automated model retraining, monitoring, drift detection, and CI/CD pipelines using tools such as MLflow, Kubeflow, and SageMaker Pipelines
Hands-on experience architecting and deploying scalable machine learning (ML) solutions on cloud platforms (e.g., AWS SageMaker, Azure Machine Learning, Google Vertex AI) with a focus on scalability, reliability, and cost optimization
Demonstrated experience leading technical architecture decisions and mentoring engineers on machine learning (ML) best practices, software engineering standards, experimentation, code quality, and research methodology
Strong background in distributed computing and big data technologies such as Apache Spark, Ray, and Dask for efficient model training and inference
Proficiency with containerization and orchestration technologies, including Docker and Kubernetes, to support scalable model serving, A/B testing, and canary releases/deployments.
Demonstrated ability to translate complex business problems into well-scoped ML solutions, communicating trade-offs, risks, and ROI to executive stakeholders
Experience contributing to or publishing applied ML research, patents, conference presentations, or open-source projects
7+ years of experience designing, developing, and deploying machine learning systems at scale in production environments

Responsibilities

Design, develop, and containerize machine learning (ML) models using modern frameworks and tools, including PyTorch, Ray, Docker, and FastAPI.
Deploy, manage, and scale production ML workloads on Kubernetes.
Integrate AI/ML capabilities into full-stack applications using Python-based backend services and JavaScript frontend technologies.
Ensure model reliability, performance, and maintainability throughout the deployment lifecycle.
Architect and implement cloud-native ML infrastructure on AWS.
Develop and maintain DevOps and MLOps pipelines to streamline model development, testing, deployment, and monitoring.
Deploy and support AI/ML systems within secure, classified, and high side environments.
Evaluate and adopt state-of-the-art AI/ML models, frameworks, and emerging technologies.
Architect scalable and resilient infrastructure to support evolving AI/ML workloads and mission requirements.
Establish and promote best practices for production-grade machine learning (ML) systems, including security, observability, and governance.
Provide technical guidance and thought leadership across AI/ML initiatives and engineering teams.