AI MLOps Engineer

About the Role

This is a hands-on, individual contributor role focused on technical execution, continuous learning, and collaboration. You will work closely with data scientists and engineers in a modern production environment, with mentorship from experienced MLOps professionals. This fully-remote role is ideal for someone who enjoys hands-on technical work, is eager to develop modern MLOps practices, and wants to contribute to real-world production systems at a tax-paying not-for-profit organization whose work directly supports our mission—peace of mind through better health.

Final interviews onsite at our Chattanooga, TN headquarters are required. Sponsorship is available for this role.

Responsibilities

• Ensuring that machine learning models are deployed efficiently and reliably into production environments.
• Continuously monitoring the performance of models to detect issues like model drift and ensure they remain accurate and effective.
• Automating the machine learning pipeline, including tasks like data preprocessing, model training, and evaluation.
• Working closely with data scientists, software engineers, and IT operations to integrate machine learning models into business processes.
• Managing version control for models and ensuring compliance with governance policies.
• Identifying and implementing ways to improve the performance and scalability of ML systems.
• Exploring cloud tools and technologies that assist data science with implementing their use cases.

Requirements

• Experience using Python for scripting, data processing, or supporting machine learning workflows.
• Experience working with a cloud-based platform (e.g., AWS, Azure, or GCP) to develop, deploy, or support data or machine learning solutions.
• Exposure to CI/CD practices, including Git-based workflows, automated testing, builds, and deployments.
• Understanding of the machine learning lifecycle, including experimentation, model versioning, and reproducibility (e.g., MLflow or similar tools).
• Foundational knowledge of data engineering concepts, such as data ingestion, transformation, validation, and storage.
• Experience contributing to simple full-stack applications, including:
  • Python-based backend APIs.
  • Basic front-end views or dashboards to display data or model outputs.
• Willingness to follow established patterns and best practices to help move ML solutions from prototype to production.

Nice to Have

• Familiarity with OpenShift or Kubernetes, and working with containerized applications.
• Experience building or maintaining infrastructure-as-code using Terraform (e.g., defining resources and managing environments).
• Exposure to Databricks for data engineering, analytics, or machine learning workflows.

Qualifications

• Bachelor’s degree in computer science or equivalent work experience (defined as 4 years of professional work experience in a corporate environment).
• 2 years of experience in software engineering and analytics technology (academic experience included).
• Experience handling large datasets to build data pipelines.
• Experience writing SQL and using data visualization tools.
• Experience solving complex problems and independently developing solutions.

Skills

• Demonstrated proficiency in languages like Python or similar.
• Strong understanding of data processing and storage solutions.
• Ability to troubleshoot issues in ML models and infrastructure.
• Ability to work independently with minimal supervision or function in a team environment sharing responsibility, roles, and accountability.
• Excellent oral and written communication skills.
• Strong interpersonal and organizational skills.