Position Responsibilities:
- Design, build, and maintain Continuous Integration/Continuous Development (CI/CD) pipelines for machine learning models
- Deploy and manage ML models in production environments using containerization and orchestration technologies
- Implement monitoring, logging, and alerting solutions to track model performance, system health, and data drift
- Collaborate with data scientists to understand model requirements and optimize the process of transforming models from development to a production-ready state
- Create and maintain technical documentation for ML Operations (Ops) processes, infrastructure, and deployments
- Define ML/Artificial Intelligence (AI) governance to ensure data security and ethical standards are met for all modeling processes
- Travel up to 5% of the time
- Other duties as assigned
Required Education and Experience:
- Bachelor’s degree in computer science, Data Science, Mathematics, or related quantitative discipline and 3 to 5 plus years of experience in ML Engineering, Software Engineering, or a related field or High School Diploma/General Education Diploma and 7 plus years of the above stated experience
Preferred Education and Experience:
- Master’s Degree in computer science, Data Science, or other graduate education in related quantitative fields
- Hands-on experience with CI/CD pipelines, automation tools, and version control systems like Azure DevOps, Github, or similar and strong understanding of machine learning concepts and the ML development lifecycle
- Experience building ML Ops infrastructure and serving models via cloud platforms such as Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP)
- Strong proficiency in Python and working knowledge of Bash/shell scripting for automation and system operations
- Strong understanding of Structured Query Language (SQL) and experience with big data platforms, i.e., Snowflake, Databricks, or similar
- Experience with Infrastructure-as-Code tools, i.e., Terraform, Azure Resource Manager, or similar