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About This Role
About this role:
Wells Fargo is seeking a Principal Engineer within Digital Technology \& Innovation supporting the Imaging Content Management Platform (ICMP) and Intelligent Document Services (IDOCS).
ICMP is one of the enterprise's most critical technology platforms, providing document management, imaging, content services, storage, retention, archival, retrieval, and governance capabilities supporting lines of business across Wells Fargo. The platform manages over 50 billion documents and serves as a foundational enterprise service for customer, operational, regulatory, and risk management processes.
This role will serve as the senior technical leader responsible for defining and executing the long\-term architecture, engineering strategy, modernization roadmap, and technical standards for ICMP and IDOCS. The Principal Engineer will lead highly scalable, secure, resilient, and compliant content management solutions while advancing intelligent document processing, automation, and AI capabilities across the enterprise.
The ideal candidate is a deeply hands\-on technologist with extensive experience in enterprise Java development, distributed systems, content management platforms, cloud\-native architectures, and large\-scale technology modernization. This individual will combine strong platform engineering expertise with strategic leadership to evolve Wells Fargo's next\-generation content and document services ecosystem.
In this role, you will provide:
Enterprise Architecture \& Platform Leadership
- Define and evolve the enterprise architecture vision and technology strategy for ICMP and IDOCS.
- Lead the design and implementation of highly scalable content management, document processing, and enterprise content service solutions.
- Establish architecture standards, engineering principles, and technology roadmaps for enterprise document repositories, content lifecycle management, storage, retention, governance, search, and retrieval capabilities.
- Lead modernization efforts across ICMP platforms, reducing technical debt while improving reliability, scalability, observability, and operational efficiency.
- Serve as the principal technical advisor to executive leadership for strategic technology investments and modernization initiatives.
Engineering Excellence
- Remain deeply hands\-on in architecture, design reviews, proof\-of\-concepts, and critical platform development activities.
- Lead complex enterprise engineering initiatives spanning multiple organizations and technology domains.
- Drive engineering rigor through automation, testing, performance optimization, observability, resiliency, and secure coding practices.
- Establish standards and best practices for Java platform engineering, microservices architecture, cloud\-native solutions, and distributed systems.
- Define enterprise integration patterns using APIs, event\-driven architectures, and shared platform services.
Content Management \& Document Services
- Lead engineering strategy for enterprise\-scale document ingestion, capture, indexing, storage, retrieval, archival, records management, and disposition capabilities.
- Establish architecture standards for document lifecycle management, metadata management, content governance, and records retention.
- Drive continuous improvement of enterprise content services supporting both business and regulatory requirements.
- Partner with cybersecurity, legal, compliance, risk, and operational stakeholders to ensure content management solutions meet enterprise governance standards.
AI \& Intelligent Document Services
- Drive intelligent document processing initiatives leveraging machine learning, AI, and Generative AI technologies.
- Lead the architecture of AI\-enabled document classification, extraction, validation, enrichment, and workflow automation capabilities within IDOCS.
- Define enterprise patterns for integrating AI capabilities into content management and document processing platforms.
- Evaluate and implement emerging AI technologies in a responsible, secure, explainable, and scalable manner.
- Guide teams from proof\-of\-concept through production deployment of AI\-powered business capabilities.
Leadership \& Influence
- Mentor engineers, architects, and technical leaders across the organization.
- Provide technical vision and thought leadership across Wells Fargo's content and document technology ecosystem.
- Partner closely with product management, platform engineering, infrastructure, security, risk, and business teams.
- Influence enterprise architecture decisions and technology standards across multiple organizations.
- Foster a culture of technology innovation, engineering excellence, accountability, and continuous learning.
Required Qualifications
- 7\+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
- 7\+ years of hands\-on software engineering experience using Java and modern JVM technologies.
- 7\+ years of experience designing and building enterprise\-scale platforms, distributed systems, and mission\-critical applications.
- 5\+ years of hands\-on experience with cloud\-native platforms, containers, Kubernetes/OpenShift, and enterprise CI/CD practices.
Desired Qualifications
- Significant experience designing, developing, and operating enterprise content management, document management, imaging, records management, or content services platforms.
- 5\+ years of experience implementing Infrastructure as Code solutions using Terraform, Crossplane, or similar technologies.
- Deep expertise in microservices architecture, RESTful APIs, event\-driven systems, and service\-oriented architectures.
- Experience building and operating highly available, resilient systems at enterprise scale.
- Experience with event\-driven architectures utilizing Kafka or similar messaging technologies.
- Strong knowledge of information security, risk management, regulatory compliance, and enterprise architecture practices.
- Proven track record leading large\-scale technology transformations with enterprise\-wide impact.
- Excellent communication, collaboration, and stakeholder management skills.
- Experience with enterprise content management, imaging, workflow, capture, archive, document management, or records management platforms.
- Deep understanding of document lifecycle management, metadata management, governance, retention, archival, legal hold, and compliance requirements.
- Experience managing large\-scale content repositories supporting billions of content objects.
- Experience designing enterprise content service platforms used across multiple lines of business.
- 3\+ years of experience architecting and implementing AI\-powered document processing solutions.
- Experience integrating Generative AI capabilities into enterprise applications and business workflows.
- Knowledge of Large Language Models (LLMs), Retrieval Augmented Generation (RAG), Agentic AI frameworks, vector databases, and enterprise AI orchestration patterns.
- Experience implementing AI governance, Responsible AI practices, and Model Risk Management (MRM) controls within regulated environments.
- Experience with cloud\-based AI platforms including Google Vertex AI, Azure AI, Gemini Enterprise, or equivalent technologies.
- Strong programming expertise in Python and related AI/automation technologies.
- Experience with relational and NoSQL database technologies including Oracle and MongoDB.
- Experience with automation platforms and DevOps tooling, including GitHub Actions, Harness, Ansible, or similar solutions.
- Experience with modern front\-end technologies including ReactJS.
- Knowledge of enterprise observability, performance engineering, and site reliability engineering practices.
- Familiarity with Wells Fargo content management and document\-processing platforms including ICMP, IDOCS, Tachyon, Capture, and related enterprise services.
- Understanding of financial services regulatory requirements governing document retention, content governance, privacy, auditability, and records management.
Job Expectations:
- This position offers a hybrid work schedule
- Commitment to Wells Fargo’s risk culture and ethical standards
- This position is not eligible for Visa sponsorship
Posting End Date:
23 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 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 $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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