• Responsible for the architecture of hybrid game and machine learning systems. • Design, develop, and maintain both learning and non-learning systems. • Stay up to date with the latest machine learning and game technologies. • Assist teams during development phases. • Create and maintain quality guidelines for machine learning applications. • Spread machine learning knowledge across the organization. • Improve team performance and the outcomes delivered.

• Responsible for the architecture of hybrid game and ML systems, serving as the primary contact for integrating machine learning into game systems • Design, develop and maintain learning and non-learning systems • Keep current on the latest developments in both ML and game technologies, identifying and exploring relevant applications for the company • Support programming and ML teams throughout development • Establish and maintain quality guidelines for our ML applications • Disseminate ML knowledge across the organization • Improve the function of one’s team, either through individual achievement or through leadership • Maintain rich, thoughtful, candid communication with peers in order to ensure the very best results • Gather, acknowledge, and respond to internal feedback, adjusting design and technological choices as necessary

• Design and maintain automated ML training pipelines. • Build infrastructure for large-scale distributed experimentation. • Develop CI/CD workflows tailored for machine learning systems. • Orchestrate data ingestion, preprocessing, validation, and model versioning. • Implement experiment tracking, hyperparameter tuning automation, and reproducibility systems. • Optimize GPU/compute utilization across cloud and on-prem environments. • Deploy, monitor, and maintain production ML models • Establish and enforce MLOps best practices including model registry, artifact management, and observability. • Improve system reliability, performance, and security. • Collaborate closely with ML researchers make new algorithms product ready. • More typical DevOps responsibilities for software development as required.

Role Description The Associate Machine Learning Engineer supports the development, testing, and deployment of machine learning models that enhance healthcare operations and patient outcomes at WVU Medicine. Working under the guidance of senior engineers and data scientists, this role applies foundational machine learning and software engineering skills to build, evaluate, and deploy ML solutions. This position contributes to CI/CD pipelines, assists with model deployment and monitoring, and supports the integration of Generative AI and large language model (LLM) capabilities into enterprise systems. Follows established engineering and MLOps practices while developing skills in building production-grade systems. The Associate Machine Learning Engineer works with a variety of healthcare data sources, including electronic medical records, claims data, and unstructured data, to support the delivery of reliable, production-ready solutions. This role is suited for early-career professionals with a strong foundation in machine learning concepts, experience programming in Python, and an interest in applying AI within healthcare. Qualifications - Bachelor’s degree in Machine Learning, Computer Science, Mathematics, Data Science, or related field AND one (1) year of professional experience in machine learning or software engineering (internships, academic research, and capstone projects qualify). - Master’s degree in a related field with no prior professional experience required. - Certifications or coursework related to machine learning, data science, or software engineering. - Hands-on experience with machine learning and LLM (large language model) frameworks. Requirements - Support data collection, cleaning, preprocessing, and feature engineering for ML model development. - Assist in training, evaluating, and validating machine learning models using Python and standard ML frameworks. - Write and maintain Python scripts and modules that are clean, documented, and production-oriented. - Contribute to CI/CD pipeline development and maintenance for automated model training, testing, and deployment in on-premise Linux-based environments. - Build and manage Docker containers for packaging ML models and dependencies for on-premise deployment on Linux-based servers. - Support productionalization of ML models, ensuring models are scalable, reliable, and ready for deployment at scale. - Assist in the integration and testing of Generative AI and LLM-powered features into healthcare applications and workflows. - Document model development processes, data pipelines, and deployment steps clearly and thoroughly. - Participate in Agile sprint ceremonies including standups, sprint planning, and retrospectives. - Collaborate with data scientists, ML engineers, and software engineers across functional teams. - Monitor deployed models for performance drift using available observability tools and contribute to retraining and improvement cycles. - Stay current with advancements in ML, Generative AI, LLMs, and open-source tools and frameworks. Benefits - Scheduled Weekly Hours: 40 - Shift: Exempt/Non-Exempt: United States of America (Exempt) Company Description West Virginia University Health System and its subsidiaries (collectively "WVUHS") is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. WVUHS strictly prohibits and does not tolerate discrimination against employees, applicants, or any other covered persons because of race, color, religion, creed, national origin or ancestry, ethnicity, sex (including gender, pregnancy, sexual orientation, and gender identity), age, physical or mental disability, citizenship, past, current, or prospective service in the uniformed services, genetic information, or any other characteristic protected under applicable federal, state, or local law. All WVUHS employees, other workers, and representatives are prohibited from engaging in unlawful discrimination. This policy applies to all terms and conditions of employment, including, but not limited to, hiring, training, promotion, discipline, compensation, benefits, and termination of employment.