MLOps Engineer: Deployments, Pipelines & Observability

Mosai; Nashville (TN); On-site; USD 140,000 - 170,000; Full time

Benefits offered by this job

null

Job summary

Mosai is seeking an experienced Machine Learning Ops (MLOps) Engineer to architect, develop, and maintain the full lifecycle of data and model pipelines powering training, inference, evaluation, and analytics workflows. The role emphasizes reliability, scalability, and observability of ML systems in production, with focus on auditing and consolidating existing pipelines.

The ideal candidate will be proficient in Python and Jupyter, deeply familiar with Snowflake and cloud environments (Azure and

Qualifications

• 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.
• Experience operationalizing traditional ML models as well as LLM-based and MCP-orchestrated systems.
• Strong working knowledge of Azure and AWS cloud platforms, including compute orchestration, networking, and security best practices.
• Experience with CI/CD tools, 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 production ML pipelines to identify anomalies, performance degradations, or failures.
• 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 ML models—including LLM-based and MCP-orchestrated systems.
• 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.

Skills

Python

Jupyter

Cross-functional collaboration

Problem-solving

Education

Bachelor's degree in CS or related field

Tools

Docker

CI/CD tools

Snowflake

Azure

AWS

Infrastructure as Code

Job description

Mosai is seeking an experienced Machine Learning Ops (MLOps) Engineer to architect, develop, and maintain the full lifecycle of data and model pipelines powering training, inference, evaluation, and analytics workflows. The role emphasizes reliability, scalability, and observability of ML systems in production, with focus on auditing and consolidating existing pipelines.

The ideal candidate will be proficient in Python and Jupyter, deeply familiar with Snowflake and cloud environments (Azure and