17 days ago
Remote, CanadaSenior
Responsibilities
- Partner with analytics and business stakeholders to identify high-value decisions, workflows, and analytical problems suitable for AI.
- Translate business needs into technical requirements, solution designs, prototypes, production-ready AI experiences, and measurable success criteria.
- Build conversational analytics experiences, AI assistants, agentic workflows, and decision-support capabilities over governed enterprise data.
- Implement retrieval, context grounding, tool calling, vector or semantic retrieval, secure APIs, and MCP integrations.
- Deliver AI use cases from discovery through validation, deployment, monitoring, adoption, and continuous improvement.
- Use semantic models, business definitions, metrics, metadata, lineage, and trusted enterprise sources to improve response reliability and traceability.
- Develop tests, evaluation scenarios, and datasets for analytical accuracy, grounding, relevance, reliability, and response quality.
- Monitor latency, cost, failures, feedback, performance, and adoption, and diagnose and resolve issues.
- Apply Responsible AI, privacy, security, identity, authorization, data governance, and human-review requirements.
- Partner with enterprise AI platform, data engineering, architecture, security, privacy, legal, and governance teams and document reusable implementation patterns.
Requirements
- Typically 5 or more years of related experience with a bachelor’s degree, 3 or more years with a master’s degree, or equivalent practical experience in software engineering, data engineering, analytics engineering, AI or ML engineering, data science, or a related field.
- Hands-on experience building and deploying production data, analytics, AI, or generative AI solutions.
- Strong Python, SQL, and software engineering skills, including APIs, testing, version control, and CI/CD practices.
- Working knowledge of LLMs, retrieval-augmented generation, AI agents, tool calling, embeddings, vector search, semantic retrieval, and enterprise knowledge integration.
- Experience with enterprise AI services or platforms such as Azure AI Foundry, Azure OpenAI, OpenAI, Anthropic, or comparable technologies.
- Experience with analytical data platforms, warehouses or lakehouses, semantic models, APIs, and BI technologies such as Microsoft Fabric, Power BI, Databricks, or Snowflake.
- Understanding of semantic and dimensional modeling, business metrics, metadata, data quality, lineage, and governed data access.
- Experience applying authentication, authorization, RBAC, identity propagation, and secure access practices to AI or data solutions.
- Understanding of LLMOps or MLOps concepts, including deployment, evaluation, monitoring, observability, and production support.
- Ability to work independently through diverse technical problems, adapt established approaches, and seek guidance at key decision points.
- Strong communication and collaboration skills with senior business and technical stakeholders.
- Preferred experience taking AI or analytics solutions from business discovery through prototyping, user testing, production deployment, and adoption.
- Preferred experience building conversational analytics or AI-enabled decision support on governed enterprise data and explaining technical tradeoffs clearly.
Benefits
- Remote, full-time employment.
- Competitive pay and benefits, with compensation varying by experience, skills, budget, location, bonuses, and incentives.
- Time-away-from-work programs, comprehensive wellness initiatives, and recognition programs.
- Professional growth opportunities and support for personal and career development.
- Volunteer days, community-impact opportunities, and Dayforce Cares charitable initiatives.
- Equal opportunity employer with accommodation support for applicants with disabilities.
About Ceridian
Ceridian, now operating as Dayforce, builds a cloud human capital management platform that unifies HR, payroll, talent, and workforce management for employers. It sells its software as a subscription and serves customers globally. Headquartered in Minneapolis, Minnesota (with a major presence in Toronto), the company traces the Dayforce brand to 2009 and is publicly traded on the NYSE after rebranding in 2024.