AI & Software Engineering Manager

Are you interested in being part of an innovative team that supports Westinghouse’s mission to provide clean energy solutions? At Westinghouse, we recognize that our employees are our most valuable asset and we seek to identify, attract and recruit the most qualified talent while recognizing and encouraging the value of diversity in the global workplace.

About the role:

As a Manager, AI & Software Engineering you will lead the AI & Software Engineering team within Data & AI, made up of AI/software engineers and UI/UX designers. The team designs production-grade AI applications (including standalone bertha™), agent-based workflows, Technology Bricks and user interfaces for Westinghouse and its customers. You will deliver the team's AI and software commitments and for developing the team.

You will report to the Director, AI Delivery and work a hybrid/mobile role based in Cranberry, PA.

Key Responsibilities:

AI application and agent engineering:

Develop and deliver AI applications and agent-based workflows, including business logic, orchestration, integrations and user interfaces. Ensure commitments are delivered on time, on budget and to the required quality.

Estimation and prioritization support:

Prepare estimates, scope assumptions and capability needs for potential new developments and commercial projects. Data & AI leadership prioritizes the portfolio and support proposals and customer commitments.

Code quality, security and maintainability:

Ensure code quality, application security, application-level monitoring and reliability.

Produce the technical documentation required for maintainability and governance, and improve findings.

Human–AI experience:

Define and apply UX and interaction guidelines so that AI solutions give users safe experiences.

Collaboration with AI & Data Infrastructure:

Work with the leadership of the AI & Data Infrastructure teams to build applications within the approved architecture, platforms, AI models, data architecture and deployment environments owned by those teams.

Contribute application requirements and feedback, and raise application-driven architecture and technology needs to them.

Architecture decisions remain with those teams.

Engineering standards:

Apply the common AI Software Development Lifecycle (AI SDLC) across the team, including code review, CI/CD, test automation, documentation and Definition of Done.

Ensure work uses the approved technology stack and follow the exception process, and that the team applies secure-by-design practices and Westinghouse software quality requirements.