Associate MLOps Engineer

BlueCross BlueShield of Tennessee•Chattanooga, TN

5d•Remote

About The Position

BlueCross BlueShield of Tennessee is looking for an Associate MLOps Engineer to support data science teams in building, deploying, and operating machine learning solutions at scale. 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. You’ll be joining a tax-paying not-for-profit organization and your work will directly contribute to our mission – peace of mind through better health. Note Final interviews onsite at our Chattanooga, TN headquarters are required for this role. Sponsorship is not available for this role.

Requirements

Bachelor’s degree in computer science or equivalent work experience required. Equivalent experience is defined as 4 years of professional work experience in a corporate environment.
2 years - 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.
Demonstrated proficiency in languages like Python or similar languages
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
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 Haves

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

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.