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About This Role
About this role:
Wells Fargo is seeking a Lead Specialty Software Engineer to support the design and delivery of technology solutions focused on modernizing and accelerating credit risk data migration. This role will provide technical leadership in the development of enterprise applications and Agentic AI solutions using modern full stack, cloud, and artificial intelligence technologies.
The Lead Specialty Software Engineer will design, develop, and implement scalable enterprise applications and Agentic AI solutions using modern software engineering technologies and frameworks. They will partner with business and technology teams to solve complex engineering challenges, contribute to technical design and architecture decisions, and drive engineering best practices across the software development lifecycle.
The ideal candidate will bring strong hands-on software engineering experience, expertise in modern application development, and experience building Agentic AI solutions. This role will also provide technical guidance and mentorship to other engineers while helping advance application modernization, automation, and responsible AI adoption.
In this role, you will:
- Design, develop, test, deploy, and support enterprise-scale applications and AI-powered solutions
- Develop and maintain full stack applications using Angular and/or React, Spring Boot, SQL Server, and related technologies
- Design and implement Agentic AI solutions using LangGraph, LLMs, Retrieval-Augmented Generation (RAG), prompt engineering, and agent orchestration frameworks
- Develop Python-based services, automation solutions, APIs, and data processing pipelines
- Collaborate with product owners, architects, business partners, and engineering teams to define and implement technical solutions
- Participate in architecture discussions, technical design reviews, and code reviews to ensure adherence to engineering standards and best practices
- Analyze and resolve complex technical challenges across application, database, integration, and AI solution components
- Contribute to CI/CD, DevOps, testing, security, and operational excellence initiatives
- Mentor and provide technical guidance to junior and mid-level engineers
- Stay current on emerging technologies, particularly in AI, software engineering, and application modernization
Required Qualifications:
- 5+ years of Specialty Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, or education
- 5+ years of experience developing Java applications using Spring Boot and microservices architectures
- 5+ years of experience developing solutions using Python
- 6+ months of experience building Agentic AI solutions using LangGraph or similar agent frameworks
Desired Qualifications:
- Experience with SQL Server, including data modeling, performance tuning, and query optimization
- Experience developing enterprise applications using Angular and/or React
- Experience working with Large Language Models (LLMs), Prompt Engineering, Retrieval-Augmented Generation (RAG), vector databases, and AI application development
- Experience designing and developing RESTful APIs and distributed systems
- Experience with Git, CI/CD pipelines, automated testing, and modern SDLC practices
- Experience with Azure, AWS, or other cloud platforms
- Experience with AI governance, observability, evaluation frameworks, and responsible AI practices
- Experience developing AI-powered productivity, automation, or modernization solutions
- Experience with semantic models, vector search, knowledge graphs, or metadata-driven architectures
- Experience with containerization technologies such as Docker and Kubernetes
- Experience working in Agile environments using Jira and modern DevOps practices
- Demonstrated ability to mentor engineers and contribute to technical leadership within a team
- Excellent verbal and written communication skills with the ability to effectively communicate technical concepts to both technical and non-technical audiences
- Strong analytical, troubleshooting, and problem-solving skills
- Passion for Agentic AI, software engineering innovation, and continuous learning
Job Expectations:
- Must be able to work onsite in one of the posted locations per the company's hybrid policy
- Serve as a senior technical contributor on strategic software engineering and AI initiatives
- Drive high-quality solution delivery through sound engineering practices and technical excellence
- Partner with business and technology teams to identify opportunities for automation, modernization, and AI adoption
- Communicate effectively with stakeholders at all levels and influence technical decisions through collaboration and expertise
- Promote a culture of innovation, ownership, continuous improvement, and engineering excellence
Posting End Date:
14 Oct 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 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 9,079 AI roles we're tracking, AI Software Engineer positions make up 5% 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 Required
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 $220,000 based on 1,601 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Wells Fargo AI Hiring
Wells Fargo has 11 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, AI Engineering Manager, AI Software Engineer. Positions span West Des Moines, IA, US, St. Louis, MO, US, Charlotte, NC, US. Compensation range: $260K - $305K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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