1 day ago
Base Salary
$116k - $181k/yr
Responsibilities
- Partner with analytics and business teams to identify high-value decisions, workflows, and analytical problems suitable for AI.
- Translate business needs into technical requirements, solution designs, prototypes, and production-ready AI experiences.
- Build conversational analytics, AI assistants, agentic workflows, retrieval-augmented experiences, and decision-support capabilities.
- Use semantic models, business definitions, metadata, governed data, documents, and knowledge bases to ground AI responses.
- Implement tool calling, vector or semantic retrieval, secure API or MCP integrations, authentication, authorization, and access controls.
- Deliver solutions from discovery and user validation through deployment, monitoring, evaluation, support, and continuous improvement.
- Develop automated evaluation scenarios for analytical accuracy, grounding, relevance, reliability, latency, cost, and adoption.
- Apply Responsible AI, privacy, security, identity, authorization, data governance, and enterprise platform standards.
- Communicate technical options, tradeoffs, progress, and risks to technical and business stakeholders 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, and version control.
- 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 and collaborate effectively with senior business and technical stakeholders.
- Preferred experience taking AI or analytics solutions from business discovery through prototype, user testing, production deployment, and adoption, particularly for conversational analytics or AI-enabled decision support.
Benefits
- Medical, dental, vision, and life insurance benefits are available to employees and their families.
- 401(k) plan with company match.
- Unlimited time away from work in lieu of accrued vacation.
- 10 paid US holidays and up to 80 hours of paid sick time.
- 17 weeks of paid parental leave, subject to applicable policy or program terms.
- Volunteer days and community impact opportunities through Dayforce Cares.
- Remote role, indicated by #LI-Remote.
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.