Senior Principal Software Developer, AI Experience

Role Description

Dayforce is building an AI Developer Experience team within Engineering to scale high-quality, agentic development practices across the organization. This team transforms early-stage experimentation into durable, reusable engineering capabilities that development teams can adopt in real codebases under real delivery pressure. The Staff Developer, AI Experience is a senior hands-on engineering role and a key contributor to the broader AI Engineering strategy. This role focuses on building the systems, standards, workflows, and reusable development capabilities that enable engineers to work more effectively with AI-assisted and agentic coding tools. The role combines architectural thinking with hands-on implementation, helping define scalable engineering patterns while working directly within repositories and delivery environments to operationalize modern AI-driven development practices.

What You’ll Get to Do

• Own and evolve reusable skill design patterns and coding artifact standards that drive AI-assisted development effectiveness across Engineering.
• Define how agent context is structured, scoped, and maintained across repositories, toolchains, and delivery pipelines.
• Design reusable context patterns including prompts, retrieval strategies, memory/state patterns, and tool exposure configurations.
• Establish practical guardrails and standards that reduce failure modes and support responsible AI-assisted development adoption.
• Build and maintain reusable AI agent skills, workflows, templates, scaffolding, and implementation guides.
• Define production-readiness standards for reusable skills including contracts, triggers, evaluation coverage, failure handling, and documentation.
• Partner directly with development teams to operationalize AI-assisted workflows in repositories, testing practices, and engineering delivery processes.
• Establish standards for emerging engineering artifacts such as AI-assisted specifications, implementation plans, and workflow patterns.
• Define evaluation and adoption criteria for scalable AI engineering capabilities including reliability, engineering value, and maintainability.
• Build data-driven visibility into AI-assisted development outcomes and ROI across Engineering.
• Define scalable implementation standards that support consistent and lightweight AI engineering adoption.
• Partner with internal platform teams and external vendors on tooling integration, feedback, and capability evolution.
• Help establish and evolve the AI Engineering enablement operating model including priorities, success metrics, and organizational scaling strategies.

Qualifications

• Strong software engineering background with experience delivering and operating complex production systems.
• Hands-on experience using agentic coding tools and AI-assisted development workflows on real engineering problems.
• Experience implementing developer workflows across repositories, testing frameworks, CI/CD pipelines, and delivery processes.
• Experience with context engineering including prompts, retrieval strategies, memory/state patterns, and LLM tooling configurations.
• Experience designing reusable agent skills with defined contracts, evaluation coverage, and operational safeguards.
• Demonstrated ability to define engineering standards, guardrails, and evaluation frameworks that improve consistency and quality across teams.
• Experience partnering directly with development teams to implement shared engineering practices.
• Strong technical judgment distinguishing scalable engineering practices from experimental concepts.
• Experience working across organizational boundaries to improve engineering effectiveness and implementation consistency.
• Excellent written communication skills with the ability to translate emerging technical practices into actionable engineering guidance.

What Would Make You Stand Out

• Experience building AI engineering enablement programs at scale.
• Strong understanding of LLM evaluation methodologies and operational reliability patterns.
• Experience designing enterprise AI developer platforms or reusable internal tooling ecosystems.
• Demonstrated ability to influence engineering standards and adoption across large organizations.
• Strong understanding of balancing experimentation, governance, scalability, and developer productivity in AI-assisted engineering environments.

Benefits

• Excellent time away from work programs.
• Comprehensive wellness initiatives.
• Recognition through competitive pay and benefits.
• Opportunities for personal and professional growth.
• Community impact initiatives, including volunteer days and charity opportunities.