Manager, AI-Enabled Development

Dayforce is a global human capital management (HCM) company headquartered in Toronto, Ontario, and Minneapolis, Minnesota, with operations across North America, Europe, Middle East, Africa (EMEA), and the Asia Pacific Japan (APJ) region.

Our award-winning Cloud HCM platform offers a unified solution database and continuous calculation engine, driving efficiency, productivity and compliance for the global workforce.

The Manager, Software Development leads a high-performing engineering team responsible for delivering reliable, scalable, and high-quality software to customers. This role combines strong people leadership, technical judgment, delivery accountability, and practical AI leadership. The manager is expected to build an environment where engineers take ownership, collaborate effectively, continuously improve their craft, and use modern AI-enabled development practices to deliver better outcomes faster.

This leader is accountable for the health, productivity, and impact of the team. They set clear expectations, guide technical direction, remove impediments, coach engineers, and ensure the team delivers software on time without compromising quality, reliability, security, or maintainability.

• Lead, coach, and develop a team of software engineers, creating a culture of ownership, accountability, collaboration, and continuous improvement.
• Drive delivery of high-quality software with performance, reliability, scalability, security, and maintainability top of mind.
• Establish, communicate, and enforce engineering standards, including code review practices, design principles, testing expectations, operational readiness, and documentation.
• Provide technical leadership and sound judgment in architectural and design decisions, especially where decisions affect other teams, platforms, customers, or the company at large.
• Guide the evolution of the team’s architecture to support future product requirements, business priorities, operational needs, and long-term scalability.
• Define and manage service-level expectations for the team’s area of ownership, including SLAs, operational metrics, reliability targets, and incident response practices.
• Partner with product, architecture, operations, security, and other engineering teams to align priorities, clarify requirements, manage dependencies, and deliver business value.
• Oversee day-to-day execution, including planning, estimation, prioritization, risk management, and delivery tracking.
• Ensure the team makes realistic commitments, delivers against those commitments, and continuously improves predictability and execution quality.
• Remove technical, organizational, and process impediments that slow the team down or reduce engineering effectiveness.
• Promote best practices in software development, including clean design, automated testing, CI/CD, observability, secure coding, and operational excellence.
• Lead adoption of AI-assisted engineering practices across the team, helping developers use AI tools effectively, responsibly, and consistently to improve productivity, quality, learning, and delivery outcomes.
• Establish clear standards for AI usage in the software development lifecycle, including code generation, code review support, testing, documentation, debugging, refactoring, design exploration, and developer enablement.
• Coach engineers on how to evaluate AI-generated output critically, validate correctness, protect sensitive information, avoid over-reliance, and maintain strong engineering judgment.
• Identify practical opportunities to use AI to improve team workflows, reduce toil, accelerate delivery, improve software quality, and increase developer effectiveness.
• Measure and improve the quality of AI adoption by tracking outcomes such as developer productivity, code quality, review effectiveness, test coverage, cycle time, defect rates, and team confidence with AI-enabled workflows.
• Stay current with emerging AI development capabilities and guide the team in adopting tools and practices that create real delivery value, not just experimentation for its own sake.

• Proven experience leading software engineering teams that deliver production software at scale.
• Strong technical background with the ability to guide architecture, design, code quality, operational readiness, and engineering best practices.
• Demonstrated ability to lead teams through planning, estimation, delivery, production support, and continuous improvement.
• Experience coaching engineers, developing talent, setting expectations, giving feedback, and supporting career growth.
• Strong communication and collaboration skills, with the ability to influence technical and non-technical stakeholders.
• Proven ability to help developers adopt AI-assisted software development practices in ways that improve delivery, quality, and engineering effectiveness.
• Practical understanding of how AI can be applied across the software development lifecycle, including coding, testing,…