Artificial Intelligence/Machine Learning (AI/ML) Engineer 2 TS/SCI w Poly

Peraton is a national security company and mission capability integrator providing technology and enterprise IT solutions to government agencies and the U.S. armed forces. The AI/ML Engineer 2 will design, develop, test, deploy, and continuously improve machine learning models and algorithms for complex business and mission requirements, while supporting data pipelines, system integration, testing, evaluation, and validation.

Responsibilities:

• Designs, develops, tests, and operationalizes algorithms to solve complex business challenges and mission requirements. Models are built to learn from data and generate predictions aligned with customer-defined business logic and operational needs
• Demonstrates proficiency across model architecture, data pipeline integration, and the application, interpretation, and presentation of performance metrics. Maintains a strong understanding of foundational concepts in application development, infrastructure management, data engineering, and data governance
• Responsible for training, retraining, deploying, scheduling, monitoring, and continuously improving models based on iterative user and system feedback to deliver scalable, high-performance solutions. May lead geographically distributed teams and frequently serve as the primary point of contact for related technical matters, requiring strong analytical thinking, problem-solving ability, and exceptional communication skills
• Experience working with various ML libraries, packages, and frameworks
• Experience with standard machine learning frameworks such as: PyTorch or TensorFlow
• Design or select appropriate data and knowledge representation methods
• Recognize and apply software architecture, data modeling, and data structure principles
• Transform data science prototypes into scalable production solutions
• Provide system integration oversight
• Select appropriate datasets for machine learning applications
• Perform statistical analysis and data interpretation
• Run and evaluate machine learning algorithms
• Use analytical results to improve and optimize models
• Train and retrain systems as required
• Research and implement a broad range of AI/ML algorithms and tools
• Verify data integrity and model output quality
• Identify data distribution differences that may impact model performance
• Run standard testing and evaluation protocols
• Oversee testing and evaluation of AI/ML algorithms through iterative design processes
• Ensure solutions meet verification and validation requirements