We are looking for a Senior Forward Deployed AI Engineer, Analytics to partner directly with analytics teams and business stakeholders to turn high-value analytical problems into practical, trustworthy AI solutions. This hands‑on role helps move the organization beyond static dashboards toward conversational analytics, intelligent insights, and AI‑enabled decision support. As a Forward Deployed AI Engineer, you will work independently on a diverse set of well‑defined and emerging problems, using sound judgment to adapt established engineering patterns to the needs of each use case. You will prototype, validate, deploy, and improve solutions in partnership with the Enterprise AI Platform, Decision Intelligence, Data Engineering, Architecture, and Governance teams. Success is measured by user trust, adoption, and measurable business value. Forward Deployed AI Engineering Partner directly with analytics and business teams to understand decisions, workflows, pain points, and analytical questions that AI can help address. Translate business needs into clear technical requirements, solution designs, prototypes, and production‑ready AI experiences. Independently 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, including BI, analytics, automation, or workflow improvements, when AI is not the best fit. AI‑Powered and Conversational Analytics Build conversational analytics experiences that allow authorized users to 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 that identify trends, anomalies, risks, opportunities, and recommended actions under established business and technical guardrails. Integrate AI‑generated insights into existing analytics tools, applications, and workflows so they are available where decisions are made. Apply judgment to determine when predictive analytics, generative AI, agents, or a combination of approaches is appropriate. Enterprise Context and Semantic Intelligence 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 gaps in data quality, semantic clarity, lineage, or access 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. Apply appropriate human review and approval controls for high‑impact insights, recommendations, and actions. Rapidly build and test prototypes to validate user value, feasibility, and trust before broader investment.



Define and measure practical success criteria such as response quality, adoption, productivity, decision cycle time, or business impact. 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; Apply established 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 feedback from deployments to improve shared platform capabilities and standards. Communicate technical options, tradeoffs, progress, and risks clearly to technical and business partners; advise stakeholders on difficult implementation matters within the role’s area of expertise. 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. Solid 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 Foundr

📌 Forward Deployed Ai Engineer Sr (Winnipeg)
🏢 Dayforce
📍 Winnipeg