AI Engineer Sr

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.

Our brand promise - Makes Work Life Better™ - Reflects our commitment to employees, customers, partners and communities globally.


About the Opportunity
We are looking for a Senior AI Engineer to join our team and help design, build, and productionize AI- and agentic-powered solutions that create measurable business value. This role is ideal for a hands-on engineer with experience in applied AI, generative AI, cloud-native development, data integration, AI enablement, and enterprise software delivery.
As a Senior AI Engineer, you will work closely with Data, Architecture, Platform, Security, and business stakeholders to transform AI opportunities into scalable, reliable, and responsible solutions. You will focus on building practical AI capabilities, enabling teams to adopt AI effectively, creating reusable engineering patterns, and helping the organization move from experimentation to value realization.
This is a hands-on technical role with strong delivery ownership. The ideal candidate is comfortable building prototypes, evaluating commercial and open-source AI models, evolving solutions into production-grade systems, and establishing engineering best practices for AI adoption across teams.
What You'll Get to Do
AI Solution Development
• Design, develop, test, and deploy AI-powered applications, services, APIs, and integrations.
• Build solutions using large language models (LLMs), embedding models, vector search, retrieval-augmented generation (RAG), prompt engineering, agentic workflows, and AI orchestration patterns.
• Translate business and product requirements into practical AI solution designs and production-ready software.
• Develop reusable components, accelerators, templates, and reference implementations for AI use cases.
• Partner with application engineering teams to integrate AI capabilities into existing products, workflows, and platforms.
• Evaluate commercial, open-source, and hybrid AI models to determine the best solution for business needs.
AI Enablement & Value Realization
• Identify, shape, and deliver AI use cases that create measurable business, operational, customer, and employee value.
• Partner with stakeholders to define success measures for AI initiatives, including productivity, automation, quality, cost reduction, adoption, and user experience.
• Support teams in moving AI initiatives from proof of concept to scalable production solutions.
• Build reusable AI enablement assets including starter kits, reference architectures, coding patterns, prompt libraries, evaluation templates, and implementation playbooks.
• Provide hands-on guidance to product and engineering teams adopting AI capabilities.
• Assess feasibility, implementation effort, business value, and complexity for proposed AI opportunities.
• Contribute to AI intake, prioritization, and value tracking processes.
• Measure post-launch solution performance and continuously improve AI capabilities based on business outcomes and customer feedback.
Agentic Workflows & AI Orchestration
• Design and build agentic workflows capable of reasoning, retrieving information, calling tools, executing business logic, and supporting human-in-the-loop decision making.
• Develop orchestration patterns for multi-step AI workflows, planning, memory, retrieval, tool usage, and task execution.
• Integrate AI agents with enterprise APIs, internal systems, workflow platforms, and business applications.
• Implement guardrails, permissions, audit trails, error handling, approval workflows, and fallback mechanisms.
• Develop solutions using frameworks such as Semantic Kernel, LangChain, LlamaIndex, AutoGen, CrewAI, or similar technologies.
• Monitor and continuously improve agent performance through evaluation frameworks, telemetry, user feedback, and business outcomes.
Production Engineering, Security & Governance
• Apply software engineering best practices including clean code, automated testing, CI/CD, observability, and secure development.
• Deploy and operate AI solutions within cloud-native environments.
• Monitor AI application performance, reliability, usage, costs, and quality.
• Troubleshoot production issues related to AI services, models, integrations, orchestration, and infrastructure.
• Implement monitoring for model outputs, retrieval quality, prompt performance, latency,