Machine Learning Operations Engineer

We are 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.

Required:

• Able to work on high-visibility or mission critical aspects of a given program and performs all functional duties independently. Must be able to oversee the efforts of less senior staff and/or be responsible for the efforts of all staff assigned to a specific job.
• Requires programming Languages: Proficient in Python, R, SQL, and scripting, experience with Deep Learning platforms (e.g., PyTorch, TensorFlow, Jupyter Notebook), and version control using Git.
• Requires an understanding of MLOps & MLFlow principles.
• Additional Skills: Understanding of containerization (Docker, Kubernetes), ability to effectively collaborate with data scientists and engineers, ability to drive Machine Learning Operations (MLOps) process to product incremental model improvements.
• Problem-Solving and Statistical Knowledge: Strong problem-solving skills as applied to Machine Learning issues, comfortable with data manipulation, analysis, and scripting.
• Degree in computer science, operations research, or related STEM field.
• Active TS/SCI security clearance. (Can start with Secret)