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About This Role
Are you looking for an exciting job where you can put your skills and talents to work at a company you can feel proud to be a part of? Do you want a workplace that will challenge you and offer you opportunities to learn and grow? A position at Xcel Energy could be just what you’re looking for.
Position Summary
The Sr. AI Engineer plays a pivotal role in advancing Xcel’s AI vision by leading the technical delivery of innovative IT and AI\-powered solutions that drive strategic value and operational excellence. This role empowers teams to leverage emerging AI capabilities such as agentic patterns, generative AI developer tooling, and secure orchestration to deliver innovative solutions that transform business operations.
In addition to technical leadership, the Sr. AI Engineer will mentor and guide teams in adopting AI tooling, establish best practices, foster a culture of continuous improvement and collaboration and ensure that AI solutions are secure, scalable, and aligned with enterprise goals. The position partners with Architecture, Security, and Operations teams to integrate enterprise architecture, compliance, and operational requirements into every solution.
The Sr. AI Engineer is responsible for leading technical aspects of IT solution delivery, strategic planning, and operational excellence. They are responsible for managing the overall health of the technology ecosystem and will manage and oversee design of solutions to meet business needs and support the overall IT Strategy. They provide technical guidance in partnership with Architecture and cross\-organizational input and in accordance with business unit objectives. They are a consultant to business managers, architecture, security and operations teams to drive cross\-awareness of business needs and opportunities. They bring innovative solutions to the business line by leveraging their technical expertise. They identify and implement continuous improvement over cost, security, operation, and functionality of the solutions in their domain.
Essential Responsibilities
- Solution Delivery: Lead and support solution lifecycle technical activities. Ensure solutions are designed for great user experience and operational performance. Lead design, ensuring Enterprise Architecture, Security, Operations and Compliance aspects are continuously integrated into solutions. Provide input to cost and schedule estimation. Responsible for overall integrity of system design and operation. Oversee vendor activities.
- Relationship Management: Conduct peer reviews and approve system changes and technical solution design. Coach and mentor less experience team members. Partner cross\-organizationally to drive minimal costs on optimal solutions. Provide in\-depth technical information to stakeholders as needed.
- Strategy \& Planning: Innovate through usage of industry emerging capabilities and evolving customer needs. Provide input to strategic roadmap and technical dependencies.
- Subject Matter Expertise: Continuously stay current on, and apply, technical industry knowledge pertaining to the respective domain
- Operations: Review solution performance, and continually assess health of systems. Track and drive awareness to operational and technical debt risks. Provide escalated support to incident and problem management. Utilize analytics to improve availability, reliability, efficiency and capacity. Oversee vendor activities.
- Solid grasp of core AI engineering concepts, including agentic patterns, agent\-to\-agent (A2A) communication, tool use, and orchestration.
- Hands\-on experience with generative AI developer tooling such as Claude Code, OpenAI Codex, GitHub Copilot, or comparable coding agents, including day\-to\-day use in a professional development workflow.
- Proficiency building with AI\-relevant languages and ecosystems, particularly TypeScript, Python, and .NET.
- Working knowledge of the Model Context Protocol (MCP) and experience with related frameworks and SDKs (e.g., FastMCP, Microsoft Agent Framework, or equivalents).
- Demonstrated experience building agents, skills, hooks, plugins, and tool integrations that extend AI platforms.
Minimum Requirements
- Bachelor's degree in Technology, Science, Business or related field, or 4 years of experience equivalent to the position.
- Ten years of related functional experience.
- Excellent communication skills, effective with varying organizational levels and skill set, and able to translate between technical and non\-technical concepts.
- Excellent Relationship Management and collaboration skills, with a track record of working as one team cross\-organizationally to drive inovation and business results.
- Expertise managing the lifecycle of technical solutions.
- Deep Subject Matter Expertise within the respective system domain products, platforms, processes and architecture.
- Broad and deep knowledge of technology architecture, infrastructure, network, security and software principles and models.
- Experience working in partnership with internal and external vendors.
- Excellent analytical, problem\-solving and troubleshooting skills.
- Extensive knowledge of future technology trends within area of expertise.
- Demonstrated leadership on technical aspects of large scale projects.
- Experience coaching other developers in system deployment or operational troubleshooting.
- Experience with delivery methodologies (Waterfall, Agile, Scrum) and operational models (ITIL).
- Experience and understanding of core IT Service Management functions, such as Change Management and Incident Management.
Preferred Requirements
- Familiarity with AI security and governance controls — approval flows, guardrails against unapproved actions, plugin/tool allowlisting, sandboxing, and permission models.
- AI Identity and data access patterns — OAuth, scoped tokens, and least\-privilege access for agents acting on behalf of users (Microsoft Graph, enterprise APIs, etc.).
- Mentorship and technical leadership — guiding teams adopting AI tooling, establishing patterns, and reviewing AI\-generated code at scale.
As a leading combination electricity and natural gas energy company, Xcel Energy offers a comprehensive portfolio of energy\-related products and services to 3\.4 million electricity and 1\.9 million natural gas customers across eight Western and Midwestern states. At Xcel Energy, we strive to be the preferred and trusted provider of the energy our customers need. If you’re ready to be a part of something big, we invite you to join our team.
All qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
Individuals with a disability who need an accommodation to apply please contact us at [email protected].
Non\-Bargaining
The anticipated starting base pay for this position is: $130,000\.00 to $170,000\.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: 06/14/26
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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/ accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact Xcel Energy Talent Acquisition at [email protected]. This contact information is for accommodation requests only and cannot be used to inquire about the status of applications.
Salary Context
This $130K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Xcel Energy, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($150K) sits 17% below the category median. Disclosed range: $130K to $170K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Xcel Energy AI Hiring
Xcel Energy has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Minneapolis, MN, US, Denver, CO, US. Compensation range: $159K - $170K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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