AI Architect

Xcel Energy (NASDAQ: XEL) is a leading energy provider and Fortune 500 company, dedicated to serving millions of electricity and natural gas customers across eight states: Minnesota, Colorado, Wisconsin, Michigan, North Dakota, South Dakota, New Mexico and Texas. We make energy work better for our customers, helping them thrive every day. That means always raising the bar - delivering better service and providing more reliable, resilient, and sustainable energy.  Xcel Energy is a place where you’ll have opportunities to grow, expand your skills, and help lead the clean energy transition. We offer meaningful rewards, recognition, and support to help you build your career and make an impact - both on the job and beyond. Most importantly, it means you’ll be joining a company that knows employees are the driving force behind its success and is committed to helping you be your best.   Xcel Energy supports a hybrid work model to enable collaboration and flexibility. Many roles are expected to work onsite three days per week (Tuesday–Thursday); however, work location expectations vary based on role, team, and business needs.

Summary The AI Architect is a mid-level, delivery-oriented solution architect responsible for designing AI solutions for assigned initiatives and supporting their implementation. The role translates validated business needs into secure, scalable, supportable, and cost-conscious architectures across generative AI and predictive machine learning. Working primarily through product owners and business analysts, the architect defines solution requirements, evaluates build-versus-buy options, establishes measurable success criteria, and validates business and technical fit. The AI Architect develops solution designs, proofs of concept, and technical validations using Google Cloud Platform (GCP), BigQuery, Databricks, and Microsoft Copilot capabilities, with limited coding where needed to reduce delivery risk. The role contributes to enterprise architecture patterns for AI assistants, Copilot integration, enterprise knowledge integration, and AI-ready data products. It collaborates with data architects on data flows, retrieval patterns, vector stores, feature pipelines, metadata, and lineage. The role applies established enterprise architecture, security, privacy, responsible AI, data governance, and model risk controls, and coordinates required reviews. Senior architects and review boards retain approval authority for material decisions, standard exceptions, and higher-risk designs. The role defines nonfunctional requirements, deployment patterns, observability, model monitoring, production-readiness criteria, and support expectations, then transitions implementation and ongoing operational ownership to engineering and operations. It also documents reusable AI patterns and lessons learned and recommends updates to enterprise standards.

Leadership: Lead architecture work for assigned initiatives through coaching and feedback, and through significant interactions and influence with delivery teams and leaders. Guide work efforts to ensure alignment with reference architecture, IT strategy and policy. Collaborate cross-organizationally to influence alignment on and adoption of enterprise architecture standards. Technical Expertise: Provide consultation and direction to delivery and operational teams on optimal architecture for AI solutions, including proofs of concept and technical validation. Maintain state-of-the-art expertise in enterprise architecture technologies, AI and machine learning frameworks, and industry innovations. Architectural Operations: Review projects and work efforts and prepare recommendations regarding compliance with architectural standards. Identify and escalate exceptions to senior architects or review boards. Continuously improve operational processes. Assess, report, and make recommendations to reduce operational risk related