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About this role:
Wells Fargo is seeking a Senior Lead Systems Operations Engineer\-ITSM AI Specialist to support the IT Service Management (ITSM) Product organization in designing and accelerating the adoption of AI\-enabled solutions across core service management offerings. This role will work directly with the ITSM Product Manager to identify, design, test, and implement fit\-for\-purpose AI capabilities across Incident, Problem, Change, Service Asset \& Configuration Management (SACM), Business Application Management, and Service Level Management, with an initial emphasis on Incident, Change, and Problem. This individual will serve as a hands\-on solution design leader and trusted advisor, partnering with Product Owners, developers, architects, platform teams, and governance stakeholders to apply the right pattern for the right problem. The role requires strong practical knowledge of AI/ML/GenAI concepts, experience translating business or operational workflows into scalable solution designs, and the judgment to determine when agentic AI is appropriate versus when simpler analytical, deterministic, or API\-based approaches are the better solution. The ideal candidate combines digital product leadership, AI solution design, experimentation, and coaching skills, and is able to accelerate product teams that are still building fluency in AI design patterns and implementation approaches.
In this role, you will:
- Partner directly with the ITSM Product Manager to design and accelerate AI\-enabled capabilities across the ITSM product portfolio, with immediate focus on Incident, Change, and Problem Management
- Translate business processes, workflow activities, and user interactions into practical AI\-enabled solution designs, including orchestration patterns, decision points, tools, and implementation approaches
- Evaluate and recommend fit\-for\-purpose solution patterns across deterministic automation, predictive models, generative AI, agentic AI, and traditional integrations or APIs
- Help define and implement AI use cases that improve customer effort, operational outcomes, and risk controls
- Develop tangible solution artifacts such as architecture recommendations, use\-case assessments, design patterns, implementation guidance, pilot recommendations, and measurement approaches
- Conduct hands\-on experimentation and prototype evaluation to validate solution feasibility, value, and operational fit
- Coach Product Owners and development teams on AI/ML/GenAI concepts, design patterns, and practical implementation tradeoffs
- Guide teams in moving from deterministic workflow thinking toward appropriate use of probabilistic and AI\-assisted solutioning where it adds value
- Prevent overuse or misuse of agentic AI by ensuring teams select the right technology pattern for the problem rather than defaulting to AI\-first solutions in all cases
- Partner with engineering, architecture, platform, and governance stakeholders to ensure AI\-enabled solutions are scalable, supportable, and aligned with enterprise standards
- Influence senior leaders and cross\-functional partners on AI strategy, implementation options, risks, and expected business outcomes within the ITSM product domain
- Support deployment of multiple AI capabilities across the six ITSM offerings, helping establish repeatable patterns that can scale across the product portfolio.
Required Qualifications:
- 7\+years of Systems Engineering, Technology Architecture experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
- Experience designing, implementing, or enabling AI, machine learning, generative AI, or intelligent automation solutions in an enterprise environment
- Experience in solution design, technical product management, or architecture\-related work translating business needs into technology solutions
- Experience working across cross\-functional teams including product, engineering, architecture, and governance stakeholders
- Experience evaluating and applying multiple solution patterns, including workflow automation, analytics, machine learning, generative AI, agentic AI, and API/integration\-based approaches
- Demonstrated ability to influence strategy, guide implementation decisions, and produce tangible solution artifacts for complex technical and business problems
- Hands\-on experience with experimentation, prototyping, or practical implementation of AI\-enabled capabilities.
Desired Qualifications:
- Strong knowledge of IT Service Management (ITSM) practices, preferably across Incident, Problem, Change, SACM, Business Application Management, or Service Level Management
- Experience with ServiceNow, preferably in ITSM and/or AI\-related capabilities on the platform
- Working knowledge of machine learning concepts such as classification, clustering, similarity modeling, and predictive techniques, with the ability to apply them appropriately to business problems
- Working knowledge of generative AI and agentic AI concepts, including orchestration, tool usage, prompt design considerations, and implementation tradeoffs
- Experience designing AI\-enabled solutions that balance business value, operational practicality, and risk management
- Ability to coach and upskill Product Owners and development teams that are early in their AI maturity
- Strong problem\-solving skills and sound architectural judgment, including knowing when a simpler deterministic or API\-based approach is preferable to GenAI or agentic AI
- Experience defining success measures for AI\-enabled solutions, including user adoption, operational performance, customer effort reduction, and risk/control effectiveness
- Strong written and verbal communication skills with the ability to explain technical concepts to both technical and non\-technical stakeholders
- Experience operating in complex, highly governed enterprise environments.
Job Expectations:
- Ability to work across a broad set of stakeholders, including Product Owners, developers, architects, governance partners, and senior leaders
- Ability to operate as an individual contributor with strong influence, coaching, and advisory responsibilities
- This role is expected to be highly hands\-on in solution design, experimentation, and implementation guidance, rather than focused primarily on roadmap ownership
- Candidate should be comfortable supporting multiple ITSM offerings simultaneously while helping establish common AI design patterns across the product
- Success in the role will be measured in part by the ability to accelerate deployment of multiple AI capabilities across the ITSM portfolio and improve product team fluency in AI solution design
- Telecommuting is not an option for this position
- This position offers a hybrid work schedule
- Relocation assistance in not available for this position
- This position is not eligible for visa sponsorship.
Posting End Date:
20 Jul 2026* *Job posting may come down early due to volume of applicants.*
We Value Equal Opportunity
Wells Fargo is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.
Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance\-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit’s risk appetite and all risk and compliance program requirements.
Candidates applying to job openings posted in Canada: Applications for employment are encouraged from all qualified candidates, including women, persons with disabilities, aboriginal peoples and visible minorities. Accommodation for applicants with disabilities is available upon request in connection with the recruitment process.
Applicants with Disabilities
To request a medical accommodation during the application or interview process, visit Disability Inclusion at Wells Fargo.
Drug and Alcohol Policy
Wells Fargo maintains a drug free workplace. Please see our Drug and Alcohol Policy to learn more.
Wells Fargo Recruitment and Hiring Requirements:
a. Third\-Party recordings are prohibited unless authorized by Wells Fargo.
b. Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Wells Fargo, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Wells Fargo AI Hiring
Wells Fargo has 13 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Architect. Positions span Minneapolis, MN, US, Chandler, AZ, US, Charlotte, NC, US. Compensation range: $239K - $305K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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