Quality Engineering Architect - MLOps

Quality Engineering Architect

At PNC, our people are our greatest differentiator and competitive advantage in the markets we serve. We are all united in delivering the best experience for our customers. We work together each day to foster an inclusive workplace culture where all of our employees feel respected, valued and have an opportunity to contribute to the company's success. As a Quality Engineering Architect within PNC's Data and Automation Technology organization, you will be based in Pittsburgh, PA, Strongsville, OH, Birmingham, AL or Dallas, TX.

We are looking for a MLOPs Quality Engineering Architect to join the Data & Automation organization, supporting the MLOps ecosystem. In this hands on individual contributor role, you will be responsible for driving scalable, automation-first validation strategies across teams, systems, and complex integrations. This role operates at both hands-on and strategic levels, influencing best practices, improving testability, and establishing reusable quality patterns that enable both design & operating effectiveness.

You will be responsible for designing, building, coordinating, executing & validating risk & control testing strategies across the MLOPs portfolio. You will work closely with QE resources, Development teams, and Platform Engineers to strengthen & standardize test coverage, automation, and end to end quality w/ evidence.

Key Responsibilities:

• Design MLOps testing strategies which verify that the rules, policies, and procedures put in place as internal controls are properly designed and operating effectively
• Champion automation, continuous testing and shift-left testing methodologies
• Build and maintain test automation and ensure stds are adopted and followed.
• Identify controls gaps and develop test harnesses to detect and validate.
• Support test data and test environment readiness across environments.
• Analyze defect trends and production incidents — contributing to root cause analysis and recommending preventive measures.
• Provide quality sign off as needed
• Contribute to QE standards and best practices for MLOps testing techniques, tools, and approaches within the ecosystem.

Key Skills &

Qualifications:

• Strong understanding of the MLOps lifecycle, including model validation, deployment, and monitoring
• Experience designing scalable test strategies across complex data and ML systems
• Expertise in test automation and continuous testing within CI/CD pipelines
• Proficiency in Python, API testing, and data validation (SQL)
• Knowledge of cloud platforms (AWS, Azure, or GCP) and distributed data systems
• Experience with risk-based testing, controls validation, and audit readiness
• Ability to analyze defects and production issues and drive root cause solutions
• Strong collaboration and stakeholder influence across engineering and QE teams
• Self-driven with a strategic mindset and hands-on execution capabilities