Senior MLOps Engineer

Role Overview

• Design, build, and maintain scalable machine learning pipelines for model deployment, validation, monitoring, and lifecycle management
• Implement model versioning, drift detection, and continuous retraining workflows to ensure model accuracy and compliance
• Collaborate with data scientists, platform engineers, and security teams to ensure reliable, secure, and efficient delivery of AI/ML capabilities
• Develop and maintain systems engineering and cybersecurity artifacts for the System
• Prepare, maintain, and execute a System Engineering Plan (SEP) for managing all systems architecture and system engineering related aspects of the program
• Conduct systems engineering activities required to specify, build, and maintain system engineering designs for the System
• Design, engineer, integrate, and continuously improve the underlying infrastructure of the System including cloud environment, network, data storage, logging, and auditing functions
• Define, document, maintain, and promulgate APIs and technical standards for using and interoperating within and outside the System
• Establish and maintain integrations with external model providers, making their available models accessible via API
• Provide Tier-4 support for any critical issues with the available services and products, in accordance with defined SLAs
• Design, architect, engineer, and continuously improve all aspects of cybersecurity elements of the System
• Perform site reliability engineering to build and maintain a reliable, scalable, and efficient System by applying software engineering principles to operational tasks
• Participate in the Engineering Control Board (ECB) process for supporting all major engineering milestones and decisions for the program

Requirements

• Bachelor’s degree in Computer Science, Engineering, or a related field
• Minimum of 8 years of experience in machine learning operations (MLOps) or related fields
• Experience with cloud platforms that host and manage infrastructure such as AWS, Azure, or Google Cloud
• Proficiency in programming languages such as Python, Java, or C++
• Experience with containerization and orchestration tools like Docker and Kubernetes
• Strong understanding of machine learning model lifecycle management
• Experience with CI/CD pipelines and version control systems like Git
• Top Secret clearance required to start
• Strong problem-solving skills and ability to work in a collaborative environment

Tech Stack

• AWS
• Azure
• Cloud
• Cyber Security
• Docker
• Java
• Kubernetes
• Python

Benefits

• Competitive compensation
• Health and Wellness programs
• Income Protection
• Paid Leave
• Retirement