About this role:
Wells Fargo is seeking a Principal Applied AI Scientist / Quant Engineer to drive the design, development, and implementation of Artificial Intelligence (AI) enabled applications aligned to the Testing, Monitoring & Audit Technology team within ERAFT (Enterprise Risk, Audit & Finance Technology). This role will focus on building intelligent, efficient, and user-centric capabilities that accelerate Testing & Monitoring outcomes across rules and metadata, sampling, execution, results analysis, reporting, workflow, evidence, and audit trails.
In addition to hands-on development, this role will play a key part in evolving reusable patterns, automation frameworks, and governance approaches for applied AI solutions in enterprise environments. Success in this role is measured by delivery of scalable capabilities, measurable quality, and reliable operational performance aligned to business objectives.
This role will be a key contributor to shaping AI infrastructure, governance, and automation frameworks, working closely with cross-functional partners in technology, product management, and operations.
In this role, you will:
Design, develop and optimize Gen AI applications using agentic frameworks and tools
Accelerate end to end solution delivery timelines by developing automated data, prompting and evaluation pipelines
Streamline the enablement of Agentic AI solutions by building solution blueprints, re-usable patterns and identifying process improvements
Lead AI engineering efforts that drive the design, development, scalability, and evolution of AI-powered products, ensuring AI adoption
Research and guide engineering efforts to solve complex engineering challenges and balance accuracy, latency and cost
Develop automated AI model monitoring frameworks, enabling continuous model updates, explainability, and performance tracking
Develop and scale AI platforms leveraging Large Language Model (LLM) services, real time analytics, AI automation, and intelligent decision-making
Act as an advisor and collaborate with leadership to integrate AI into existing enterprise systems and cloud platforms to implement innovative and significant business solutions
Drive cross-functional collaboration to define AI roadmaps, infrastructure strategies, and product enhancements, ensuring AI capabilities align with business AI strategies
Lead the strategy and resolution of highly complex and unique challenges requiring in-depth evaluation across multiple areas or the enterprise, delivering solutions that require vision, creativity, innovation, and advanced analytical and thinking
Maintain knowledge of industry best practices and new technologies and recommend innovations that enhance operations or provide a competitive advantage to the organization
Strategically engage with all levels of professionals and managers across the enterprise and serve as an expert advisor to leadership
Responsible for meticulous governance to address the unique risks posed by GenAI
Lead technical workstreams and serve as a peer mentor
Desired Qualifications:
Strong expertise in AI model development and deployment with a strong background in LLMs, generative AI, and AI-engineering
Deep experience with generative AI models, including model prompting, tuning, and safety best practices
Expertise in solution architecture and applying modular design techniques to agentic workflows
Solid grasp of data and error analysis, identifying issues and patterns throughout the AI pipeline
Strong expertise in test or eval driven development, ensuring robust and scalable AI software
Experience in backend application software development, with ability to quickly adapt to C#, and Python code bases
Strong understanding of Retrieval-Augmented Generation (RAG), knowledge graphs and agentic workflows
Deep knowledge of AI infrastructure, Generative AI Operations, and enterprise-scale AI adoption strategies
Familiarity with enterprise-scale software systems and their integration within large organizations
Passion for building AI solutions that deliver a seamless, end-user-focused experience
Experience in enterprise AI model lifecycle management, AI compliance, and risk mitigation strategies
Strong understanding of human centered AI design for workplace applications
Excellent collaboration, communication, and problem-solving skills
Nice to have: prior experience with knowledge graph technologies and the design and validation of semantic matching frameworks in enterprise environments.