About the opportunity 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. What you’ll get to do 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. Agentic Analytics Engineering 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 a user’s authorized access. Apply appropriate human review and approval controls for high-impact insights, recommendations, and actions. Prototype to Production Delivery 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. 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. Governance and Platform Partnership Use enterprise-approved models, platforms, security patterns, connectors, and reusable components rather than creating isolated point solutions. 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. Skills and experience we value 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 working 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. What would make you stand out You have taken an AI or analytics solution from business discovery through prototype, user testing, production deployment, and adoption. You can work comfortably with both business stakeholders and technical teams, translating between decisions, data, and implementation details. You have built conversational analytics or AI-enabled decision support on top of governed enterprise data. You understand why semantic context, data quality, lineage, and access controls are essential to trustworthy enterprise AI. You are pragmatic about using the simplest technology that solves the business problem and can explain tradeoffs clearly. You share lessons learned and reusable patterns that help other teams deliver AI solutions more effectively.