Senior Forward Deployed AI Engineer Analytics

Dayforce

11d•$116,000 - $181,300•Remote

About The Position

Dayforce is seeking a Senior Forward Deployed AI Engineer to partner with analytics teams and business stakeholders to turn high-value analytical problems into practical, trustworthy AI solutions. The role helps move the organization beyond static dashboards toward conversational analytics, intelligent insights, and AI-enabled decision support. The engineer works independently on defined and emerging problems, adapting established engineering patterns to each use case, and partners with Enterprise AI Platform, Decision Intelligence, Data Engineering, Architecture, and Governance teams. Success is measured by user trust, adoption, and measurable business value. The role spans discovery, solution design, prototyping, validation, production deployment, and continuous improvement. It includes building conversational analytics experiences and AI agents grounded in governed enterprise data and semantic context, integrating AI-generated insights into existing tools and workflows, and applying appropriate security, privacy, access, and Responsible AI controls. Solutions are developed using enterprise-approved platforms and reusable patterns, with testing, evaluation, monitoring, and production support built into delivery. The position is remote, within the Enterprise Data Analytics AI and Governance department, and is full-time. Dayforce offers medical, dental, vision, and life insurance; a 401(k) plan with matching; unlimited time away from work; paid US holidays; paid sick time; paid parental leave; and opportunities for community involvement.

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 one or more 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, Snowflake, or equivalents.
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 when appropriate, and seek guidance at key decision points.
Strong communication and collaboration skills, including the ability to work effectively with senior business and technical stakeholders.

Nice To Haves

Experience taking an AI or analytics solution from business discovery through prototype, user testing, production deployment, and adoption.
Ability to work comfortably with business stakeholders and technical teams, translating between decisions, data, and implementation details.
Experience building conversational analytics or AI-enabled decision support on governed enterprise data.
Understanding of the importance of semantic context, data quality, lineage, and access controls for trustworthy enterprise AI.
A pragmatic approach to selecting the simplest technology that solves the business problem and explaining tradeoffs clearly.
Experience sharing lessons learned and reusable patterns to help teams deliver AI solutions more effectively.

Responsibilities

Partner with analytics and business teams to understand decisions, workflows, pain points, and analytical questions that AI can address.
Translate business needs into technical requirements, solution designs, prototypes, and production-ready AI experiences.
Deliver assigned AI use cases independently from discovery through user validation, deployment, and ongoing improvement; escalate material architecture, security, or platform decisions when needed.
Use feedback and usage data to improve solution accuracy, relevance, reliability, and adoption.
Recommend non-AI alternatives, including BI, analytics, automation, or workflow improvements, when they are a better fit.
Build conversational analytics experiences that let authorized users ask natural-language questions of trusted enterprise data.
Develop AI assistants and agentic workflows that retrieve business context, query governed analytical data, interpret results, and provide understandable responses.
Build capabilities to identify trends, anomalies, risks, opportunities, and recommended actions within established business and technical guardrails.
Integrate AI-generated insights into existing analytics tools, applications, and workflows.
Select appropriate predictive analytics, generative AI, agentic, or combined approaches for each use case.
Use enterprise-approved AI assistants and coding tools in development, requirements synthesis, prototyping, documentation, testing, troubleshooting, and solution improvement; independently review and validate their outputs.
Protect sensitive enterprise information, follow access and Responsible AI requirements, and remain accountable for engineering decisions and delivered solution quality.
Use semantic models, business definitions, metrics, metadata, and relationships to ground AI solutions in trusted enterprise context.
Implement retrieval and context patterns across structured data, documents, knowledge bases, and approved enterprise information sources.
Work with data architects, governance specialists, and analytics teams to address data quality, semantic clarity, lineage, or access gaps that affect AI reliability.
Ensure AI responses can be traced to appropriate enterprise data sources and business definitions when required.
Develop AI agents and workflows using approved orchestration frameworks, enterprise AI services, APIs, and data platforms.
Implement tool calling, retrieval-augmented generation, vector or semantic retrieval, and secure API or MCP integrations using established enterprise patterns.
Enable agents to interact securely with semantic models, datasets, knowledge sources, and enterprise services within users’ authorized access.
Apply appropriate human review and approval controls for high-impact insights, recommendations, and actions.
Build and test prototypes to validate user value, feasibility, and trust before broader investment.
Define and measure success criteria such as response quality, adoption, productivity, decision cycle time, or business impact.
Engineer approved solutions for production reliability, scalability, security, observability, and supportability using enterprise standards.
Develop automated tests and evaluation scenarios for AI-generated responses, including analytical accuracy, grounding, and relevance.
Monitor performance, latency, cost, failures, feedback, and adoption; diagnose issues and implement improvements within established support practices.
Use approved models, platforms, security patterns, connectors, and reusable components rather than creating isolated point solutions.
Apply Responsible AI, privacy, security, data governance, identity, and authorization requirements throughout solution delivery.
Partner with Enterprise AI Platform, Security, Privacy, Legal, and Data Governance teams on exceptions, risks, and complex integrations.
Document reusable implementation patterns and provide deployment feedback to improve shared platform capabilities and standards.
Communicate technical options, tradeoffs, progress, and risks clearly, and advise stakeholders on difficult implementation matters within the role’s area of expertise.