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About This Role
About this role:
Wells Fargo is seeking a Principal Engineer for the Digital Technology \& Innovation team, specifically within Enterprise Content Management (ECM), focusing on Intelligent Document Services (IDOCS). IDOCS is described as one of the most robust, mission\-critical, high\-scale production platforms in the enterprise, managing over 50 billion documents and acting as a trusted AI system. This role requires a hands\-on technologist with deep Java ecosystem expertise to architect, design, develop, and scale enterprise\-grade AI systems, operating at a global banking scale within a highly regulated financial environment. The Principal Engineer for IDOCS will lead the architecture and design of next\-generation AI solutions, transforming document operations such as classification, extraction, validation, and enrichment, and will play a pivotal role in shaping the adoption and scaling of new AI tools and technologies across the enterprise.
In this role, you will:
- Act as a technical advisor to leadership to develop or influence complex applications, network, information security, database, operating systems, or web technologies across multiple groups.
- Lead the strategy and resolution of highly complex and unique challenges, requiring in\-depth evaluation across multiple areas or the enterprise, delivering long\-term, large\-scale solutions that demand vision, creativity, innovation, advanced analytical, and inductive thinking.
- Translate advanced technology experience and in\-depth organizational knowledge into technical engineering solutions, aligning with business objectives and strategic technological opportunities.
- Provide vision, direction, and expertise to leadership on implementing innovative and significant business solutions for the IDOCS platform.
- Maintain knowledge of industry best practices and new technologies, recommending innovations that enhance operations or provide a competitive advantage to the organization.
- Strategically engage with all levels of professionals and managers across the enterprise, serving as an expert advisor to leadership, particularly on AI strategy and technical decisions related to IDOCS.
- Remain deeply hands\-on, designing, reviewing, and occasionally coding critical components to set a high engineering bar for IDOCS. This includes leading deep technical design reviews across backend, API, and AI integration layers.
- Lead by example in applying engineering rigor, automation, testing, performance optimization, and secure coding practices within the IDOCS development lifecycle.
- Drive the strategy, adoption, and governance of AI and GenAI capabilities across the IDOCS organization, partnering with product, data, risk, and security teams to ensure responsible, explainable, and scalable AI solutions.
- Architect scalable, secure, and compliant AI infrastructure for IDOCS across hybrid environments.
- Oversee end\-to\-end development lifecycle for IDOCS, including feature engineering, model development, validation, deployment, and monitoring, implementing proactive, event\-driven model monitoring and drift detection.
- Integrate Large Language Models (LLMs), RAG pipelines, agent frameworks, and AI orchestration frameworks into enterprise Java systems and foundational IDOCS platform components.
- Mentor senior engineers and architects, raising the overall technical maturity of the organization and elevating engineering rigor across the IDOCS team.
Required Qualifications:
- 7\+ years of Engineering experience, or equivalent demonstrated through work experience, training, military experience, or education
- 7\+ years of engineering experience with sustained hands\-on design and development in Java (modern JVM stacks, microservices, distributed systems, performance engineering)
- 5\+ years of hands\-on programming and/or scripting experience in Python or other relevant languages
- 2\+ years of experience with AI, Generative AI, or Agentic automation solutions design and development
Desired Qualifications:
- 5\+ years of experience with Infrastructure as Code (IaC) implementation using Terraform, Crossplane, or other industry\-equivalent solutions
- 5\+ years of experience with OpenShift Container Platform and/or Google Cloud Platform, and/or Microsoft Azure hands\-on experience
- 4\+ experience architecting and building AI\-powered solutions, including LLM integration, prompt engineering, RAG patterns, model orchestration, AI pipelines, and responsible AI practices
- Hands\-on experience with Vector Databases and their application in knowledge representation and retrieval
- Experience with event\-driven architectures (Kafka or similar)
- Strong background in cloud\-native architectures (containerization, orchestration, CI/CD, DevSecOps) with experience operating systems at enterprise scale
- Proven experience designing large\-scale distributed systems in production
- Experience leading complex technology initiatives, including those that are companywide with broad impact
- 2\+ years of experience building full stack Agentic AI Automations (from chat experiences to multi\-agent systems) using Agentic AI Frameworks like LangGraph, Crew AI, Microsoft AutoGen, LangChain,etc.
- Deep understanding of LLMs, prompt engineering, vector databases, and orchestration tools
- Expertise in cloud AI platforms (GCP Vertex AI, Azure ML), On\-Prem AIML systems, and MLOps frameworks
- Strong programming skills (Python, SQL/NoSQL) and experience with ML frameworks (TensorFlow, PyTorch)
- Proven track record in enterprise\-scale AI deployments, model migration projects, and innovation initiatives
- Exposure to Google Cloud Platform: Vertex AI / Gemini Enterprise Agent Platform, Agentspace, MCP, A2A exposure
- Experience with enterprise\-grade automation solutions design and implementation using tools such as Ansible, Harness CD, GitHub Actions, or Playwright
- Familiarity with responsible AI principles, governance, and operating within regulated environments, including Model Risk Management (MRM) processes and controls
- Experience with database technologies such as Oracle, Mongo, with 3\+ years of MongoDB experience
- Experience using ReactJS
- Strong analytical skills with high attention to detail and accuracy
- Excellent communication and stakeholder management skills, with the ability to articulate complex technical concepts to executives, regulators, partners, and engineering teams
- Knowledge of Wells Fargo's internal systems and processes related to content management (e.g., ICMP, Tachyon, Capture)
Job Expectations:
- This position offers a hybrid work schedule
- This position is not eligible for Visa sponsorship
- Commitment to Wells Fargo’s risk culture and ethical standards
Posting End Date:
1 Jun 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Wells Fargo AI Hiring
Wells Fargo has 16 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, AI Product Manager, AI Agent Developer. Positions span Iselin, NJ, US, Charlotte, NC, US, Minneapolis, MN, US. Compensation range: $196K - $305K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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