AI Architect

Xcel Energy•Minneapolis, MN

4d•Hybrid

About The Position

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.

Requirements

Seven years of related functional experience.
Bachelor's degree in Technology, Science, Business or related field, or 4 years of experience equivalent to the position.
Exposure to diverse systems, technologies and processing environments in a large Corporate setting.
Project solution architecture experience.
Knowledge of core aspects of architecture (Software, Hardware, Network, Integration, Data, hosting models, platform types, etc.).
Strong analytical, problem-solving and troubleshooting skills.
Extensive knowledge of future technology trends within area of expertise.
Excellent communication skills, effective with varying organizational levels and skill sets.
Excellent Relationship Management and collaboration skills, with a track record of working as one team cross-organizationally to drive innovation and business results.

Nice To Haves

Experience architecting and governing Microsoft Copilot and enterprise AI solutions, including AI control planes, enterprise data access, security, governance, and AI-ready data products.
Working experience designing cloud-based AI and machine learning solutions, supported by strong cloud solution architecture fundamentals.
Experience with generative AI and predictive machine learning patterns, including retrieval-augmented generation (RAG), agents, model serving, evaluation, and monitoring.
Experience with MLOps practices, including model lifecycle management, experimentation, deployment, observability, performance monitoring, and operational transition.
Experience with GCP, BigQuery, and Databricks for AI, machine learning, analytics, or data engineering solutions.
Experience creating proofs of concept, performing technical validation, and using limited coding to test architecture decisions and reduce delivery risk.
Experience defining measurable solution success criteria, evaluating model and solution quality, validating business and technical fit, and supporting build-versus-buy decisions.
Experience designing AI-related data flows, retrieval patterns, vector stores, feature pipelines, metadata, lineage, and governed access in collaboration with data architects.
Working knowledge of responsible AI, cybersecurity, privacy, data governance, and model risk practices, including identifying risks, incorporating established controls, and coordinating required reviews.
Experience defining nonfunctional requirements, deployment patterns, scalability, latency, resiliency, observability, model monitoring, production-readiness criteria, and support expectations.
Experience collaborating with engineering and FinOps on model and service selection, consumption forecasting, performance, scalability, and cost optimization.
Ability to translate business needs provided through product owners and business analysts into architecture requirements, evaluation criteria, and implementable solution designs.
Experience documenting reusable AI architecture patterns, capturing lessons learned, and recommending improvements to standards and reference architectures.
Relevant GCP, Databricks, cloud architecture, machine learning, or AI certifications are preferred but not required.

Responsibilities

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.
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.
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 to technology or architecture.