Machine Learning Operations Engineer

Mosai•Nashville, TN

5d•Hybrid

About The Position

Mosai™ is the intelligent care coordination platform that brings together the fragmented pieces of healthcare into a clear, connected picture. Like a mosaic, our platform unites data, people, and processes so providers can make better decisions, coordinate care in real time, and deliver improved outcomes. With Mosai, home-based care organizations can thrive in value-based care while giving every patient the right care, in the right place, at the right time. Learn more at https://www.mosai.com/ We are seeking an experienced Machine Learning Ops (MLOps) Engineer to architect, develop, and maintain the full lifecycle of data and model pipelines that power training, inference, evaluation, and analytics workflows. This role is responsible for ensuring the reliability, scalability, and observability of all machine learning systems in production, including traditional ML models and modern LLM-based/MCP-orchestrated architectures. A key focus of this role in the near term is auditing and consolidating our existing pipelines and deployment processes. The ideal candidate is highly skilled in Python, Jupyter, Snowflake, and both Azure and AWS cloud environments, and thrives in environments requiring continuous monitoring, rapid issue diagnosis, and rigorous validation before deployment.

Requirements

Bachelor’s Degree in Computer Science, Engineering or equivalent work experience.
5–7 years of combined experience in Data Engineering, MLOps, Machine Learning Engineering, or related fields.
Demonstrated experience operationalizing traditional ML models as well as LLM-based and MCP-orchestrated systems.
Strong working knowledge of both Azure and AWS cloud platforms, including compute orchestration, networking, and security best practices.
Experience with CI/CD tools, containerization (Docker), infrastructure-as-code, and ML pipeline frameworks.
Strong ability to diagnose and resolve pipeline failures, data anomalies, and complex system issues.
Advanced proficiency in Python, Jupyter, and common ML/analytics frameworks.
Hands-on experience with Snowflake or similar cloud data warehousing enviro
Excellent problem-solving skills, attention to detail, and a proactive, self-directed work ethic.
Strong communication skills and comfort working in fast-paced, cross-functional environments.

Responsibilities

Design, build, and maintain scalable data pipelines supporting model training, inference, batch processing, and real-time analytics workflows.
Audit, refactor, and consolidate existing ML pipelines and deployment processes to eliminate technical debt, redundant workflows, and undocumented manual steps.
Monitor and deploy and deploy 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.