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About This Role
About this role:
Wells Fargo is seeking a Specialty Software Engineer to support the onboarding, adoption, and enablement of AI tools and software development lifecycle (SDLC) technologies across the Chief Data Office (CDO). This role will focus on accelerating engineering productivity, fostering technical communities, and promoting innovative practices through events such as hackathons and internal forums.
In this role, you will:
- Drive onboarding, adoption, and effective usage of AI tools and SDLC tooling across engineering teams
- Provide technical leadership and guidance on tooling standards, best practices, and integration patterns
- Lead complex initiatives related to developer platforms, AI tooling, and engineering productivity
- Design, develop, test, and implement tools, utilities, and automation that enhance developer experience and operational efficiency
- Mentor junior engineers and provide guidance on tool usage and engineering best practices
- Influence team members and stakeholders to adopt new tools and capabilities
- Onboarding repos — run repos through the qualifying criteria, create environment blueprints, generate codebase wikis, run proof\-of\-concept PRs
- Building reusable assets — author org\-wide and repo\-specific playbooks, knowledge notes, and scheduled automations
- Running the champions program — train and support per\-team tools champions, run 30\-min team onboarding workshops, hold office hours
- Driving adoption — identify high\-value use cases per team, pair with developers, lower the barrier to starting sessions (Teams/GitHub entry points)
- Measuring \& reporting — own the adoption metrics dashboard (active repos, unique users, PRs/week, merge rate), report ROI to leadership
- Governance \& best practices — define standards for how a tool is used safely (review requirements, security guardrails, what tasks are/aren't appropriate)
- Feedback loop — surface platform gaps/friction back to the vendor and internal infra teams
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, education
- 5\+ years of AI Engineering experience
- Hands\-on experience using one or more of the following tools: Devin.AI, Claude Code, Cursor.AI, or Github Copilot
- Hands\-on experience with AI software development tools, CI/CD pipelines, and SDLC processes
- Hands\-on experience building or supporting GenAI applications, including prompt design and RAG concepts
- Familiarity with AI/ML frameworks or orchestration tools (LangChain or similar)
- Experience integrating applications with APIs, data platforms, or enterprise systems
- Understanding of cloud\-based AI services (GCP Vertex AI, AWS, or Azure equivalents)
- Experience supporting or implementing developer tooling, platforms, or engineering enablement capabilities
- Strong general software engineering — fluency across your org's main stacks (Python/FastAPI, Scala/Spark, Java/Spring, TypeScript/React) so they can validate Devin's output across teams
- CI/CD \& DevOps — GitHub Actions/Enterprise, build/test pipelines, Docker/Kubernetes; needed to wire repos for reliable Devin sessions
- Environment configuration — writing environment blueprints (dependency setup, build/test/lint commands), managing secrets, package registries (Artifactory)
- Testing \& verification — designing how task completion is measured (test coverage, automated checks) so Devin work is verifiable
- Git \& code review at scale — branch strategy, PR review, merge\-conflict resolution
Desired Qualifications:
- Prompt \& playbook engineering — building reusable macros/playbooks, scoping tasks well for agents
- Understanding agent capabilities \& limits — knowing what tools do does well vs. poorly
- Knowledge/context management — capturing coding conventions, architecture, gotchas as persistent knowledge notes
- Developer advocacy / enablement — running workshops, demos, office hours; this is a people\-facing adoption role, not just engineering
- Technical writing \& documentation
- Metrics \& ROI storytelling — tracking adoption, PR merge rates, time saved, and reporting to leadership
- Stakeholder management \& change management — driving behavior change across teams
- Security \& compliance awareness — (PII handling, approval gates, audit)
- Experience in financial services or other regulated industries
Job Expectations:
- This position is not eligible for visa sponsorship
- Ability to work on\-site in one of the listed locations in a hybrid model. There is no option for fully remote work.
Posting End Date:
19 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 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 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 $232,000 based on 797 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 $200,100. 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, Chandler, AZ, 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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