AI​/ML Engineer; Clearance Required

Responsibilities Job Responsibilities

• Model Development & Deployment
• 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.
• Infrastructure & Operations
• Architect and implement cloud-native ML infrastructure on AWS.
• Develop and maintain Dev Ops and MLOps pipelines to streamline model development, testing, deployment, and monitoring.
• Deploy and support AI/ML systems within secure, classified, and high side environments.
• Technical Leadership
• 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, governance.
• Provide technical guidance across AI/ML initiatives and engineering teams.

• Active Top Secret/SCI (TS/SCI) clearance with a current Polygraph
• Bachelor’s degree with 3 years of related experience; OR Master's degree with 1 years of related experience; OR associate’s degree with 6 years of related experience; OR High School diploma/GED with 9 years of related experience
• Demonstrated experience deploying machine learning (ML) models to production, including large language models (LLMs)
• Demonstrated experience with machine learning (ML) frameworks and containerization technologies (e.g., PyTorch, Docker, and Kubernetes)
• Full-stack software development experience using Python and Java Script
• Working knowledge of AWS cloud services and infrastructure
• Demonstrated experience implementing MLOps and Dev Ops best practices
• U.S. Citizenship is required

• Proficiency in Python with hands‑on experience using leading machine learning frameworks, including Tensor Flow, PyTorch, or scikit‑learn
• Experience designing and implementing end-to-end machine learning (ML) pipelines, including data preprocessing, feature engineering
• Familiarity with cloud-based machine learning (ML) platforms such as AWS Sage Maker, Azure Machine Learning, or Google Vertex AI
• Understanding of MLOps best practices, including model versioning, experiment tracking, model monitoring, and CI/CD for machine learning (ML) workflows using tools such as MLflow, Weights & Biases, or DVC
• Ability to collaborate with cross‑functional teams including data engineers, product managers, and domain experts to translate business problems into ML solutions
• Experience working with large‑scale datasets and distributed computing frameworks such as Apache Spark or Dask to support efficient data processing and model training
• Familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes) for scalable model serving and orchestration
• Strong communication skills with the ability to explain model behavior, trade‑offs, and results to both technical and non‑technical stakeholders

All qualified applicants will receive consideration for employment without regard to race, color, ethnicity, sex, age, national origin, religion, physical or mental disability, pregnancy/childbirth and related medical conditions, veteran or military status, or any other characteristics protected by applicable federal, state, or local law.

If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact us.

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