Forward Deployed AI Engineer Sr - Analytics

Dayforce

1d • $116,000 - $181,300 • Remote

About The Position

Dayforce is seeking a Senior Forward Deployed AI Engineer, Analytics, 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, applying established engineering patterns to each use case. In partnership with Enterprise AI Platform, Decision Intelligence, Data Engineering, Architecture, and Governance teams, the engineer prototypes, validates, deploys, and improves solutions. Success is measured by user trust, adoption, and measurable business value.

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 equivalent.
• 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.
• Comfort working with business stakeholders and technical teams and 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 to trustworthy enterprise AI.
• A pragmatic approach to choosing the simplest technology that solves the business problem and explaining tradeoffs clearly.
• Willingness to share lessons learned and reusable patterns to help other teams deliver AI solutions more effectively.

Responsibilities

• Partner with analytics and business teams to understand decisions, workflows, pain points, and analytical questions that AI could address.
• Translate business needs into technical requirements, solution designs, prototypes, and production-ready AI experiences.
• Deliver assigned AI use cases from discovery through user validation, deployment, and ongoing improvement, escalating material architecture, security, or platform decisions when needed.
• Use feedback and usage data to improve solution accuracy, relevance, reliability, and adoption.
• Recommend simpler alternatives, such as BI, analytics, automation, or workflow improvements, when AI is not the best 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.
• 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 identify data quality, semantic clarity, lineage, or access gaps affecting 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 according to a user’s authorized access.
• Apply 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 solution performance, latency, cost, failures, feedback, and adoption; diagnose issues and implement improvements within established support practices.
• Use enterprise-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 to address exceptions, risks, and complex integration needs.
• Document reusable implementation patterns and provide deployment feedback to improve shared platform capabilities and standards.
• Communicate technical options, tradeoffs, progress, and risks clearly to technical and business partners, and advise stakeholders on difficult implementation matters within the role’s area of expertise.

Benefits

• Medical, dental, vision, and life insurance.
• 401(k) plan with company match.
• Unlimited Time Away From Work in lieu of accrued vacation time.
• 10 paid US holidays.
• Up to 80 hours of paid sick time.
• 17 weeks of paid parental leave, subject to the terms of the applicable policy or program.
• Volunteer days and opportunities to participate in Dayforce Cares.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior