Mosai

Machine Learning Engineer

• Design, build, and maintain scalable data pipelines supporting model training, inference, batch processing, and real-time analytics workflows.
• Monitor production ML pipelines to identify anomalies, performance degradations, or failures related to data quality, logic defects, or infrastructure issues.
• Execute rapid troubleshooting and root-cause analysis followed by timely remediation, validation, and full regression testing prior to redeployment.
• Collaborate with Data Science, Engineering, and Product teams to operationalize machine learning models—including LLM-based and MCP-orchestrated systems—ensuring seamless integration into production environments.
• Develop CI/CD workflows, model deployment strategies, and automated testing frameworks to support reliable, repeatable releases.
• Implement and maintain observability tooling (logging, monitoring, alerting) to ensure high availability and traceability of ML systems.
• Manage and optimize cloud infrastructure across Azure and AWS for compute, storage, orchestration, and security needs.
• Create and maintain documentation, runbooks, and best practices for model operations and system maintenance.
• Perform all other job-related duties as assigned.

Machine Learning Operations Engineer

Full-stack Engineer

• Provide innovative and scalable solutions that better enable our clients to focus on patient care.
• Ability to complete all phases of software development life cycle including R&D, scoping, design, development, testing and support.
• Work closely with members of the product team to achieve the best user experience while writing high quality, scalable code.
• Own features/projects beginning to end from mockups and design to review and deployment.
• All other duties related to this position.

Machine Learning Operations Engineer

Software Engineer