{"id":3300895,"url":"https://alion.io/job/dayforce-forward-deployed-ai-engineer-sr-analytics","title":"Forward Deployed AI Engineer Sr - Analytics","company":{"id":175960,"name":"Dayforce","domain":"dayforce.com","url":"https://alion.io/company/dayforce","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Dayforce","truth_index":{"grade":"B","score":77,"open_postings":15,"ghost_share":0,"stale_share":0.933,"repost_share":0,"time_to_fill_p50_days":36,"computed_at":"2026-10-29T05:49:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":134000,"max_usd":257000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1332},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"LLMOps","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"OpenAI","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-26T05:00:00Z","employer_posted_date":"2026-10-26","last_verified_at":"2026-10-29T05:44:40Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"About the opportunity\nWe 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.\nAs 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.\nWhat you’ll get to do\nForward Deployed AI Engineering\nPartner directly with analytics and business teams to understand decisions, workflows, pain points, and analytical questions that AI can help address.\nTranslate business needs into clear technical requirements, solution designs, prototypes, and production-ready AI experiences.\nIndependently deliver assigned AI use cases from discovery through user validation, deployment, and ongoing improvement, escalating material architecture, security, or platform decisions when needed.\nUse feedback and usage data to improve solution accuracy, relevance, reliability, and adoption.\nRecommend simpler alternatives, including BI, analytics, automation, or workflow improvements, when AI is not the best fit.\nAI-Powered and Conversational Analytics\nBuild conversational analytics experiences that allow authorized users to ask natural-language questions of trusted enterprise data.\nDevelop AI assistants and agentic workflows that retrieve business context, query governed analytical data, interpret results, and provide understandable responses.\nBuild capabilities that identify trends, anomalies, risks, opportunities, and recommended actions under established business and technical guardrails.\nIntegrate AI-generated insights into existing analytics tools, applications, and workflows so they are available where decisions are made.\nApply judgment to determine when predictive analytics, generative AI, agents, or a combination of approaches is appropriate.\nEnterprise Context and Semantic Intelligence\nUse semantic models, business definitions, metrics, metadata, and relationships to ground AI solutions in trusted enterprise context.\nImplement retrieval and context patterns across structured data, documents, knowledge bases, and approved enterprise information sources.\nWork with data architects, governance specialists, and analytics teams to identify gaps in data quality, semantic clarity, lineage, or access that affect AI reliability.\nEnsure AI responses can be traced to appropriate enterprise data sources and business definitions when required.\nAgentic Analytics Engineering\nDevelop AI agents and workflows using approved orchestration frameworks, enterprise AI services, APIs, and data platforms.\nImplement tool calling, retrieval-augmented generation, vector or semantic retrieval, and secure API or MCP integrations using established enterprise patterns.\nEnable agents to interact securely with semantic models, datasets, knowledge sources, and enterprise services within a user’s authorized access.\nApply appropriate human review and approval controls for high-impact insights, recommendations, and actions.\nPrototype to Production Delivery\nRapidly build and test prototypes to validate user value, feasibility, and trust before broader investment.\nDefine and measure practical success criteria such as response quality, adoption, productivity, decision cycle time, or business impact.\nEngineer approved solutions for production reliability, scalability, security, observability, and supportability using enterprise standards.\nDevelop automated tests and evaluation scenarios for AI-generated responses, including analytical accuracy, grounding, and relevance.\nMonitor solution performance, latency, cost, failures, feedback, and adoption; diagnose issues and implement improvements within established support practices.\nGovernance and Platform Partnership\nUse enterprise-approved models, platforms, security patterns, connectors, and reusable components rather than creating isolated point solutions.\nApply established Responsible AI, privacy, security, data governance, identity, and authorization requirements throughout solution delivery.\nPartner with Enterprise AI Platform, Security, Privacy, Legal, and Data Governance teams to address exceptions, risks, and complex integration needs.\nDocument reusable implementation patterns and provide feedback from deployments to improve shared platform capabilities and standards.\nCommunicate 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.\nSkills and experience we value\nTypically 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.\nHands-on experience building and deploying production data, analytics, AI, or Generative AI solutions.\nStrong Python, SQL, and software engineering skills, including APIs, testing, version control, and CI/CD practices.\nWorking knowledge of LLMs, retrieval-augmented generation, AI agents, tool calling, embeddings, vector search, semantic retrieval, and enterprise knowledge integration.\nExperience with one or more enterprise AI services or platforms such as Azure AI Foundry, Azure OpenAI, OpenAI, Anthropic, or comparable technologies.\nExperience working with analytical data platforms, warehouses or lakehouses, semantic models, APIs, and BI technologies such as Microsoft Fabric, Power BI, Databricks, Snowflake, or equivalent.\nUnderstanding of semantic and dimensional modeling, business metrics, metadata, data quality, lineage, and governed data access.\nExperience applying authentication, authorization, RBAC, identity propagation, and secure access practices to AI or data solutions.\nUnderstanding of LLMOps or MLOps concepts, including deployment, evaluation, monitoring, observability, and production support.\nAbility to work independently through diverse technical problems, adapt established approaches when appropriate, and seek guidance at key decision points.\nStrong communication and collaboration skills, including the ability to work effectively with senior business and technical stakeholders.\nWhat would make you stand out\nYou have taken an AI or analytics solution from business discovery through prototype, user testing, production deployment, and adoption.\nYou can work comfortably with both business stakeholders and technical teams, translating between decisions, data, and implementation details.\nYou have built conversational analytics or AI-enabled decision support on top of governed enterprise data.\nYou understand why semantic context, data quality, lineage, and access controls are essential to trustworthy enterprise AI.\nYou are pragmatic about using the simplest technology that solves the business problem and can explain tradeoffs clearly.\nYou share lessons learned and reusable patterns that help other teams deliver AI solutions more 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