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. - Commitment to community impact, including volunteer days and charity initiatives.