Machine Learning Operations (MLOps) Engineer - Advisor

at  Peraton

ABOUT PERATON

Peraton is a next-generation national security company that drives missions of consequence spanning the globe and extending to the farthest reaches of the galaxy. As the world’s leading mission capability integrator and transformative enterprise IT provider, we deliver trusted, highly differentiated solutions and technologies to protect our nation and allies. Peraton operates at the critical nexus between traditional and nontraditional threats across all domains: land, sea, space, air, and cyberspace. The company serves as a valued partner to essential government agencies and supports every branch of the U.S. armed forces. Each day, our employees do the can’t be done by solving the most daunting challenges facing our customers. Visit peraton.com to learn how we’re keeping people around the world safe and secure.

Peraton is seeking a Machine Learning Operations (MLOps) Engineer to support the Multi Domain Task Force at Joint Base Lewis McChord, WA. As an MLOps Engineer, you will apply your understanding of data networks, databases, and cloud architecture to collect, gather, process, store, and provide timely data. This work will be CI/CD and MLOps based, supporting the data-driven decision process.

Tasks include:

• Requirements Collaboration: Collaborates closely with customers, machine learning, and data science teams to thoroughly understand data science project requirements and objectives.
• MLOps Leadership: Champions practices and tools for managing the end-to-end lifecycle of machine learning models. Includes: Data Versioning and Management, Model Versioning, Model Training and Validation Pipelines, Model Deployment and Monitoring, Infrastructure as Code (IaC), Collaboration and Governance, Security and Compliance.
• CI/CD Support: Takes the lead in implementing robust Continuous Integration (CI) and Continuous Deployment (CD) code management pipelines for machine learning models. Ensures seamless automation of critical development stages, resulting in high-quality software releases and reduced errors.
• Model Lifecycle Management: Ensures proper model training, validation, deployment, and ongoing monitoring. Maintains lifecycle model health and performance.
• Infrastructure Design: Establishes infrastructure tailored to project requirements and constraints, enabling efficient model development and deployment.