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About this role:
Wells Fargo is seeking a talented Lead Software Engineer in Digital \& Advisory Technology as part of Wealth, Investment and Brokerage group. Together we work closely to continue evolving Wells Fargo’s digital advisor platforms and enhance integration of the innovation pipeline into our financial advisors and customer\-facing capabilities. It all begins with outstanding talent, and it all begins with you.
About this role:
We are expanding our Digital \& Advisory Technology team with an AI\-Enabled Lead Software Engineer to build intelligent systems that enhance client advice, financial planning, and investment decision\-making.
In this role, you will partner with financial advisors, data scientists, and product teams to deliver scalable, AI\-driven solutions across digital advisory channels. The ideal candidate would have very strong technical knowledge of Java, Kafka and a solid foundation in micro service design methodologies. You will work at the intersection of software engineering, machine learning, and financial strategy, developing personalized recommendations, predictive planning tools, and automated investment platforms. This role is ideal for engineers with strong software expertise and a passion for embedding AI into enterprise\-scale solutions.
In this role, you will:
- Lead moderately complex initiatives and deliverables within technical domain environments
- Utilize GenAI and AI Agents to build reusable libraries that can improve products development efficiency
- Monitor model performance, manage feedback loops, and ensure ethical AI practices
- Leader on scrum team helping to design and implement solutions to maximize client value
- Embrace a product mindset as a key technical input to help drive product strategy, discovery, and definition throughout the year
- Key contributions such as new features, repositories, and products are being discovered and developed
- Proactively engaging with Engineering Managers, Principal Engineers, and Architects to raise issues, areas of concern, best practices, and lessons learned
- Design, code, test, debug, and document for projects and programs associated with technology domain, including upgrades and deployments
- Resolve moderately complex issues and lead a team to meet existing client needs or potential new clients’ needs while leveraging solid understanding of the function, policies, procedures, or compliance requirements
- Collaborate and consult with peers, colleagues, and mid\-level managers to resolve technical challenges and achieve goals
- Lead projects and act as an escalation point, provide guidance and direction to less experienced staff
- Lead complex technology initiatives including those that are companywide with broad impact
- Act as a key participant in developing standards and companywide best practices for engineering complex and large scale technology solutions for technology engineering disciplines
- Design, code, test, debug, and document for projects and programs
- Review and analyze complex, large\-scale technology solutions for tactical and strategic business objectives, enterprise technological environment, and technical challenges that require in\-depth evaluation of multiple factors, including intangibles or unprecedented technical factors
- Make decisions in developing standard and companywide best practices for engineering and technology solutions requiring understanding of industry best practices and new technologies, influencing and leading technology team to meet deliverables and drive new initiatives
- Collaborate and consult with key technical experts, senior technology team, and external industry groups to resolve complex technical issues and achieve goals
- Lead projects, teams, or serve as a peer mentor
Required Qualifications:
- 5\+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
- 5 \+ years of strong technical skills in Java and supporting build/development tools such as Springboot and Gradle
- 3\+ years of experience in Apache Kafka/Confluent Enterprise
Desired Qualifications:
- A bachelor’s or master’s degree in computer science, data science, mathematics, or related field
- Experience with the Financial Services industry terminology and procedures to allow close collaboration with business partners and financial advisors.
- Hand\-on skills for AI agent and Model Context Protocol (MCP) skills
- Experience with AI\-powered development or GitHub Copilot
- Strong experience working on Java, Spring Boot, Kafka, Microservices, and API development.
- Experience working with MongoDB and Oracle Database technologies
- Knowledge and understanding of test driven and behavior driven application development.
- Experience working in an Agile Principles and Scrum environment
- Experience with Postman and/or Swagger UI
- Experience with automated testing development
- Experience with CI /CD tools and processes including GitHub, Jenkins, Artifactory, and Harness
- Ability to help triage complex technical support issues In\-depth technical solution knowledge, including installation, configuration, performance tuning
- Quick learner passionate about learning new and emerging technologies
Job Expectations:
- Ability to work onsite at one of the posted locations
- Visa sponsorship is not available for this position
Pay Range
Reflected is the base pay range offered for this position. Pay may vary depending on factors including but not limited to demonstrated examples of prior performance, skills, experience, or work location. Employees may also be eligible for incentive opportunities.
$119,000\.00 \- $206,000\.00Benefits
Wells Fargo provides eligible employees with a comprehensive set of benefits, many of which are listed below. Visit Benefits \- Wells Fargo Jobs for an overview of the following benefit plans and programs offered to employees.
- Health benefits
- 401(k) Plan
- Paid time off
- Disability benefits
- Life insurance, critical illness insurance, and accident insurance
- Parental leave
- Critical caregiving leave
- Discounts and savings
- Commuter benefits
- Tuition reimbursement
- Scholarships for dependent children
- Adoption reimbursement
Posting End Date:
7 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.
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.
Salary Context
This $119K-$206K range is below the median for AI Software Engineer roles in our dataset (median: $190K across 219 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Wells Fargo, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills in Demand for This Role
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $232,000 based on 797 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($162K) sits 30% below the category median. Disclosed range: $119K to $206K.
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.
Wells Fargo AI Hiring
Wells Fargo has 23 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Safety, AI Product Manager. Positions span Charlotte, NC, US, New York, NY, US, Irving, TX, US. Compensation range: $140K - $305K.
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 Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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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