AI/ML Engineer - Multiple levels (Cleared)

Noblis•Reston, VA

5d•Hybrid

About The Position

Noblis is seeking AI/ML Engineers at all experience levels with an active TS/SCI with a Polygraph to support mission-critical national security initiatives. In this role, you will design, develop, and deploy advanced machine learning and AI solutions while building the scalable infrastructure needed to operationalize AI capabilities in secure, production environments.

Requirements

Active Top Secret/SCI (TS/SCI) with Polygraph
U.S. Citizenship is required
Travel up to 10% within US.
Lift up to 30 lbs, walk, bend, drive.
Bachelor's, Master's, or PhD degree with 0-3 years of related experience including exposure or coursework in machine learning concepts and frameworks; OR Associate's degree with 3 years of related experience; OR High School diploma/GED with 6 years of related experience (Junior level)
Knowledge of machine learning frameworks and deploying ML models to production (Junior level)
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 (Mid-level)
Experience with machine learning frameworks and deploying ML models to production (Mid-level)
Bachelor's degree with 8 years of related experience; OR Master's degree with 6 years of related experience; OR Associate's degree with 11 years of related experience; OR High School diploma/GED with 14 years of related experience (Senior level)
Experience with machine learning frameworks and deploying ML models to production (Senior level)

Nice To Haves

Full-stack software development experience using Python and JavaScript
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

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 integrate state-of-the-art AI/ML models, frameworks, and emerging technologies to enhance mission capabilities and accelerate innovation
Architect scalable, resilient, and secure infrastructure to support evolving AI/ML workloads, production deployments, and mission-critical requirements
Establish and champion best practices for production-grade machine learning (ML) systems, including MLOps, security, observability, and governance
Provide technical guidance and thought leadership across AI/ML initiatives and engineering teams