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About This Role
Wells Fargo is back in the office collaborating for fabulous outcomes!
This is a hybrid role and in the office three days a week.
There are no Visa sponsorship or Visa transfers.
About this Role
You are someone with demonstrated experience designing, deploying, and managing enterprise AI/LLM solutions in production, including RAG architecture, cloud platforms, vector databases, APIs, containerization, monitoring, governance, and security controls.
The Senior Lead AI \& Risk Analytics Analyst is responsible for designing and operationalizing AI\-enabled analytical solutions that enhance the identification, monitoring, and mitigation of emerging risks across the enterprise. This role combines expertise in risk analytics, prompt engineering, data visualization, and cross\-functional collaboration to transform complex business and risk data into actionable intelligence.
The individual will develop advanced AI applications and agentic workflows, partner with engineers to design and deploy AI agents, and create executive\-level dashboards, drill\-through capabilities, and analytical visualizations that detect trends, root causes, concentrations, and emerging risk patterns. The role serves as a strategic advisor to business, risk, technology, and governance partners while helping establish scalable AI\-driven analytical capabilities.
Key Responsibilities
AI Solution Development and Technical Architecture Implementation
- Design, develop, and deploy production\-ready AI applications using generative AI techniques, including large language models (LLMs), retrieval\-augmented generation (RAG), and agentic frameworks, to support risk identification, issue analysis, thematic reviews, and executing reporting.
- Build intelligence automation solutions (prompt libraries, governance standards, testing methodologies, reusable AI assets) that enhance data quality risk analysis and governance, and support business operational efficiency.
- Develop prompt engineering frameworks and fine\-tuning strategies for domain\-specific LLM applications.
- Create conversational AI interfaces, intelligent assistants and APIs to integrate AI applications into existing data risk platforms, for the full usage from non\-technical stakeholders.
- Architecture and implement RAG pipelines for knowledge retrieval from structured and unstructured financial data sources. Evaluate AI outputs for accuracy, explainability, consistency, and adherence to enterprise risk and AI governance requirements.
- Optimize data storage and retrieval mechanisms for high\-performance AI applications.
- Stay current with emerging AI technologies and evaluate their applicability to data quality risk governance needs.
- Work with compliance and risk management teams to ensure AI solutions meet regulatory and governance requirements.
Risk Analytics \& Emerging Risk Detection
- Lead the analysis of large, complex datasets to identify emerging risks, systemic trends, control weaknesses, and root causes.
- Develop frameworks that leverage AI\-generated insights to support proactive risk management and decision\-making.
- Translate analytical findings into actionable recommendations that improve control effectiveness and risk mitigation.
- Create methodologies for detecting recurring patterns across issues, defects, incidents, controls, and other risk\-related data sources.
- Maintain expertise in current and emerging risk trends and integrate those insights into analytical solutions.
Visualization \& Business Intelligence
- Design and develop executive dashboards, scorecards, visual analytics, and drill\-through reporting capabilities.
- Create interactive visualizations that enable leaders to investigate trends, concentrations, impacts, and emerging risk indicators.
- Build scalable reporting solutions that provide transparency into risk exposure, issue management performance, and remediation effectiveness.
- Define key risk indicators (KRIs), metrics, and thresholds to support proactive monitoring.
- Present complex analytical concepts through clear, actionable, and executive\-ready storytelling.
Strategic Leadership \& Stakeholder Management
- Lead complex, cross\-functional initiatives involving Risk, Data Management, Technology, Compliance, Audit, and Business partners.
- Act as a trusted advisor to senior leaders on AI\-enabled risk analytics strategies and opportunities.
- Communicate analytical findings, recommendations, and emerging risks to executive audiences.
- Influence strategic decisions regarding AI adoption, risk monitoring capabilities, and analytical maturity.
- Mentor analysts and contribute to the development of enterprise analytical best practices.
Required Qualifications:
- 7\+ years of Data Management, Business Analysis, Analytics, or Project Management experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education.
- 3\+ years of AI prompt engineering, AI agent development, advanced risk analytics and executive reporting, with strong capabilities in translating complex risk data into AI\-enabled insights, dashboards, and decision\-support solutions.
- Strong academic foundation in Computer Science, Data Science, Machine Learning, Artificial Intelligence, or related quantitative field.
- Hands\-on experience deploying and supporting AI/LLM applications in production environments, including application architecture, scalability, monitoring, and operational support.
- Experience with AI orchestration frameworks, tools such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, and large language models (GPT, Claude, Llama, Gemini, etc.), or similar technologies.
- Experience implementing cloud\-native AI solutions using Azure, AWS, or Google Cloud platforms, including enterprise AI services and APIs.
- Hands\-on experience designing and implementing Retrieval\-Augmented Generation (RAG) solutions utilizing vector databases, semantic search, and knowledge retrieval architectures.
- Experience developing and deploying APIs, microservices, and containerized applications using technologies such as FastAPI, Docker, Kubernetes, and CI/CD pipelines.
- Knowledge of AI/LLMOps practices including model evaluation, prompt optimization, version control, observability, performance monitoring, and governance controls.
- Understanding of AI security, responsible AI principles, model risk management, data privacy, explainability, and regulatory compliance within highly regulated environments.
Desired Qualifications:
- Proven track record of leading complex, cross\-functional initiatives focused on data quality, issue remediation, and process/control improvements.
- Familiarity with data governance and data management tooling (e.g., data quality success metrics, data lineage, issue tracking) and experience in partnership with business and tech teams.
- Strong executive presence with the ability to influence stakeholders and drive alignment in a matrixed environment.
- Experience designing, implementing, or evolving enterprise data governance operating models.
- Advanced analytical and problem\-solving skills, with the ability to structure ambiguous challenges and deliver actionable insights.
- Strong proficiency in Python, SQL, and front\-end development.
Posting End Date:
30 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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