AI Engineer Sr

Dayforce is a global human capital management company headquartered in Toronto, Ontario, and Minneapolis, Minnesota. They are seeking a Senior AI Engineer to design, build, and productionize AI-powered solutions that deliver measurable business value, focusing on collaboration with various teams to create scalable and responsible AI capabilities.

Responsibilities:

• 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
• 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
• 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
• 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, token usage, and user feedback
• Partner with Security, Legal, Risk, and Governance teams to ensure AI solutions meet enterprise standards
• Promote responsible AI practices by implementing controls for privacy, security, auditability, human oversight, and data protection
• Identify and mitigate risks related to hallucinations, bias, misuse, data leakage, explainability, and unsafe agent behavior
• Provide technical guidance to engineers and delivery teams working on AI initiatives
• Lead design discussions and contribute to broader architecture decisions
• Stay current on emerging AI technologies, frameworks, and engineering best practices
• Communicate technical concepts clearly to both technical and non-technical stakeholders
• Help build organizational AI knowledge by sharing reusable assets, implementation guidance, and lessons learned

Requirements:

• 6+ years of experience in software engineering, machine learning engineering, data engineering, or related technical roles
• 2+ years of hands-on experience building AI, machine learning, automation, or intelligent workflow solutions
• Strong programming experience in Python and at least one additional language such as Java, C#, TypeScript, or Go
• Hands-on experience with generative AI, large language models, prompt engineering, embeddings, vector search, retrieval-augmented generation, or AI orchestration
• Experience building production-grade APIs, services, integrations, and backend systems
• Experience with Azure, AWS, or another major cloud platform
• Strong understanding of software engineering fundamentals including system design, testing, CI/CD, monitoring, and operational support
• Experience working with structured and unstructured data, data pipelines, APIs, and enterprise data sources
• Ability to evaluate trade-offs across accuracy, performance, scalability, privacy, security, maintainability, cost, and business value
• Ability to connect technical delivery to measurable business outcomes
• Strong communication, collaboration, and problem-solving skills
• Experience building enterprise generative AI solutions using frameworks such as LangChain, LlamaIndex, Hugging Face, Azure OpenAI, OpenAI APIs, Claude, AutoGen, CrewAI, or similar technologies
• Experience evaluating, fine-tuning, hosting, or deploying open-source models
• Experience with small language models, domain-specific models, embedding models, or hybrid model architectures
• Knowledge of agentic orchestration platforms and intelligent workflow automation
• Experience with vector databases or search platforms such as Azure AI Search, Pinecone, OpenSearch, or Elasticsearch
• Experience with LLM evaluation, prompt management, experiment tracking, or AI observability
• Experience designing secure enterprise AI solutions in SaaS, regulated, or large-scale environments
• Familiarity with responsible AI principles, AI governance, data privacy, and secure software development
• Experience mentoring engineers or leading technical delivery of complex AI initiatives