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About This Role
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. Position 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.
Essential Responsibilities
- 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 to technology or architecture.
Minimum 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.
Preferred Requirements
- 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.
Our culture is grounded in shared values and employee commitments that guide how we work, support one another, and grow together.
Our Values
Connected, Committed, Trustworthy, Safe
Our Employee Commitments
- Work that makes a difference
- A team you can count of
- Opportunities to grow
- Rewards that help you thrive
Xcel Energy provides job applicants reasonable accommodations for disabilities throughout the application process. If you are in need of an accommodation to complete the application process, now or at any time in the future, please complete the Applicant Accommodation Request Form.
Non-Bargaining
The anticipated starting base pay for this position is: $118,000.00 to $142,500.00 per year
This position is eligible for the following benefits: Annual Incentive Program, Medical/Pharmacy Plan, Dental, Vision, Life Insurance, Dependent Care Reimbursement Account, Health Care Reimbursement Account, Health Savings Account (HSA) (if enrolled in eligible health plan), Limited-Purpose FSA (if enrolled in eligible health plan and HSA), Transportation Reimbursement Account, Short-term disability (STD), Long-term disability (LTD), Employee Assistance Program (EAP), Fitness Center Reimbursement (if enrolled in eligible health plan), Tuition reimbursement, Transit programs, Employee recognition program, Pension, 401(k) plan, Paid time off (PTO), Holidays, Volunteer Paid Time Off (VPTO), Parental Leave
Benefit plans are subject to change and Xcel Energy has the right to end, suspend, or amend any of its plans, at any time, in whole or in part.
In any materials you submit, you may redact or remove age-identifying information including but not limited to dates of school attendance and graduation. You will not be penalized for redacting or removing this information.
Deadline to Apply: 10/16/26
EEO is the Law (PDF) | Equal Opportunity Statement (PDF) | Employee Rights (PDF)
All Xcel Energy employees and contractors share responsibility for protecting the company's information and systems by adhering to cybersecurity policies, standards, and best practices, recognizing that cybersecurity is everyone's responsibility.
Accessibility Statement
Xcel Energy endeavors to make https://www.xcelenergy.com and https://jobs.xcelenergy.com accessible to any and all users. If you are in need of an accommodation to complete the application process, now or at any time in the future, please complete the Applicant Accommodation Request Form. For any other application related questions, including application follow-ups or technical issues, please use our Recruiting Assistance, Electra, for assistance. Electra can be found in the lower right-hand corner of our Xcel Energy Careers site.
Salary Context
This $118K-$142K range is in the lower quartile for AI Architect roles in our dataset (median: $175K across 37 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 9,079 AI roles we're tracking, AI Architect positions make up 1% of the market. At Xcel Energy, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $224,070 based on 236 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($130K) sits 42% below the category median. Disclosed range: $118K to $142K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Xcel Energy AI Hiring
Xcel Energy has 1 open AI role right now. They're hiring across AI Architect. Based in Minneapolis, MN, US. Compensation range: $142K - $142K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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