MLOPS ENGINEER

Job ID: 250001T8
Status: Full-Time
Regular/Temporary: Regular
Shift: Day Job
Facility: UPMC Health Plan
Department: HEALTH ECONOMICS
Location: Pittsburgh, PA
Union Position: No
Salary Range: $ 43.47-73.65 USD
UPMC Health Plan is seeking a skilled and motivated MLOps Engineer to join our AI Solutions team. In this role, you will be responsible for designing, building, and maintaining the infrastructure and tools that enable the deployment, monitoring, and scaling of machine learning models in production. You will work closely with data scientists, software engineers, and Cloud Engineering teams to streamline the ML lifecycle and ensure robust, secure, and efficient model operations.

QUALIFICATIONS:

• Masters degree in Computer Science, Engineering, or a related field or Bachelors in related fields with 5+ years relevant professional experience.
• 3+ years of experience in MLOps, DevOps or ML Engineering roles. Preference will be given to those with MLOps experience.

3 YEARS RELEVANT WORK EXPERIENCE AFTER COMPLETION OF TERMINAL DEGREE IS HIGHLY PREFERRED.

• Experience deploying and building data science and machine learning solutions.
• Proficiency with ML lifecycle tools and orchestration frameworks.
• Hands on experience with cloud platforms (Azure preferred)
• Solid understanding of CI/CD principles.
• Familiarity with drift monitoring, logging and alert frameworks.LICENSURE, CERTIFICATIONS, AND CLEARANCES:
• Act 34

• Develop and maintain CI/CD pipelines for machine learning workflows, including model training, validation, deployment, and monitoring.
• Collaborate with data scientists to productionize ML models and ensure reproducibility and scalability.
• Design and implement model versioning, model registry, and automated retraining strategies.
• Monitor model performance and data drift in production environments and implement alerting and rollback mechanisms.
• Optimize infrastructure for cost, performance, and reliability using cloud-native tools and services (e.g., AWS, Azure, GCP).
• Ensure compliance with data governance, security, and privacy standards throughout the ML lifecycle.
• Automate data ingestion, feature engineering, and model evaluation processes.
• Contribute to the development of internal tools and frameworks to support experimentation and deployment.
• Document best practices and provide guidance to cross-functional teams on MLOps principles.