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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.
AI Workflow Automation Lead
Position Summary
The Enterprise Risk AI Workflow Automation Lead is a senior individual contributor responsible for turning a substantial share of Enterprise Risk work into scalable AI\-enabled operating models. The role translates strategic intent, objective\-state designs, and early proof\-of\-concept work into governed solutions that improve cycle time, consistency, traceability, and organizational reach.
This role sits at the intersection of risk operations, enterprise systems, and applied AI execution. It designs agentic workflows that retrieve information, reason over structured and unstructured data, interact with internal platforms, approved plugins and connectors, and use LLM APIs and selected system actions while preserving human review, escalation, and control requirements. The role also evaluates when plugin\-based integrations are appropriate, defines usage boundaries, and ensures those integrations remain reliable, governed, and auditable. The role also tracks current AI capability limits, forecasts where those limits may move over the next three to six months, and sequences implementation accordingly.
Essential Responsibilities
- Workflow Transformation: Convert current\-state Enterprise Risk processes, target\-state operating concepts, and early prototypes into backend AI workflows that reduce manual effort and increase departmental throughput.
- Enterprise Risk System Integration: Define and implement interactions between agentic workflows and internal systems, including document repositories, workflow and ticketing tools, reporting solutions, structured data sources, knowledge repositories, and approved plugins or connectors that extend AI workflows into enterprise tools and content environments.
- LLM API \& Tool\-Calling Design: Translate business workflow needs into implementation requirements involving LLM APIs, approved plugins and connectors, selected internal and external APIs, structured data exchange, tool\-calling patterns, permissions, logging, and safe execution boundaries. Partner with technical teams when deeper engineering support is needed.
- Human / AI Boundary \& Handoff Design: Establish the design for human and AI interaction, create protocols for review points, approval gates, exceptions, escalation, and accountability, define expectations for human oversight, and periodically update those patterns as technology and tools evolve.
- AI Boundary Forecasting: Continuously evaluate what current AI tools can reliably automate, document failure patterns, and forecast where practical capability boundaries may move over the next three to six months, so implementation plans stay aligned with the next wave of usable functionality.
Minimum Requirements
- Bachelor’s degree in business, analytics, information systems, computer science, operations, engineering, economics, or a related discipline;
- Or Seven years of an equivalent combination of education and relevant experience.
- Relevant experience implementing AI\-enabled workflows, agentic systems, automation solutions, enterprise knowledge workflows, or adjacent process\-transformation capabilities in a business environment.
- Working knowledge of LLM model APIs, approved plugin and connector integration patterns, authentication and permissions concepts, structured and unstructured data exchange, and safe execution controls for agentic workflows.
- Demonstrated ability to establish human and AI operating boundaries, create protocols and expectations for handoffs and review, and update those protocols as capabilities evolve.
- Ability to assess current AI capability boundaries, recognize material failure modes, and translate a three\-to\-six\-month capability outlook into a staged implementation roadmap.
Preferred Requirements
- Experience designing or operating multi\-step agentic workflows that retrieve information, reason over data, call APIs or tools, and generate auditable outputs across enterprise environments.
- Experience with AI evaluation, testing harnesses, prompt and version management, failure\-mode documentation, model or agent monitoring, and human\-in\-the\-loop quality assurance.
- Experience interfacing AI workflows with internal systems such as document management platforms, workflow or ticketing tools, enterprise knowledge retrieval solutions, reporting environments, structured data sources, and approved plugins or connectors that support enterprise workflow execution.
- Experience writing Python automation scripts or moderate\-complexity internal code to support AI workflow orchestration, tool calling, or prototype\-to\-production implementation.
- Familiarity with Microsoft enterprise tooling such as Power Platform, Power Automate, Power BI, Copilot, related plugin or connector ecosystems, or comparable enterprise AI and automation ecosystems.
- Experience creating operating protocols, review expectations, or implementation standards for human and AI collaboration in controlled environments.
- Experience within Enterprise Risk Management, Compliance, Internal Audit, Controls, Governance, utilities, energy, or another highly regulated environment.
- Understanding of AI governance and risk management frameworks.
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: $112,200\.00 to $159,400\.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: 06/15/26
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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 $112K-$159K range is in the lower quartile 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 ($135K) sits 25% below the category median. Disclosed range: $112K to $159K.
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
AI roles in Denver pay a median of $184,000 across 159 tracked positions. That's 8% below the national 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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