AI Engineer

• Lead the product strategy, roadmap, and delivery of AI-enabled solutions, translating complex business challenges into scalable products that drive measurable operational and customer outcomes.
• Define and optimize human-AI interaction models, including conversational experiences, prompt strategies, decision-support tools, and human-in-the-loop workflows that improve adoption, trust, and productivity.
• Partner closely with Engineering, Data Science, Operations, and Risk/Governance teams to prioritize opportunities, define requirements, and deliver AI products from concept through deployment and continuous optimization.
• Own product discovery and solution design by translating business needs into AI use cases, product requirements, user stories, success metrics, and implementation plans aligned to enterprise objectives.
• Balance innovation with governance by ensuring AI solutions adhere to enterprise controls, regulatory requirements, risk frameworks, and responsible AI principles within a highly regulated environment.
• Establish scalable product frameworks, reusable AI patterns, and best practices that accelerate AI transformation initiatives, modernize workflows, and unlock sustainable business value across the organization.
• Partner with oversight and governance contacts within ECRL as well as enterprise teams such as the COO Gen AI COE and Model Risk Management (MRM) on design validation, AI use case development, and model ownership.
• Serve as the user experience design lead on AI solutions, leaning on design thinking to establish fit for purpose delivery of technology solutions tied directly to operational goals.
• Partner with the product delivery team to ensure intent of design is maintained through delivery, testing is completed, and software development lifecycle (SDLC) and model development lifecycle (MDLC) requirements are met.
• Develop an integrated view of customer experiences across the Enterprise, ensuring interdependencies across organizations and functions are understood and map business capabilities to the business systems managed by IT to identify and resolve inconsistencies, duplications or gaps.
• Acts as a liaison between client area and technical organization by planning, conducting, and directing the analysis of current state and future state design.
• Conduct people, process, data, logic/ calculation and systems deep dive current state assessments to ensure all redundancies, deficiencies, failure points and constraints are understood and addressed.
• Recommend solutions from a people, process, business intelligence and technology perspective to standardize, streamline, and install appropriate control mechanisms for enhanced business performance, customer excellence, productivity, improved quality, etc.
• Conduct change impact analysis and develop capability gap closure roadmaps including short- and long-term implementation recommendations.
• Ensure target state alignment between the business model, operating model, organizational structure and IT solutions and capabilities.
• Consults with Enterprise Architects to ensure integration across domains.
• Identify risk through these transformations and evaluate and install appropriate control mechanisms to minimize risk.
• Perceives the impact and implications of decisions on other components of the company as well as impact on investors, customers and clients.
• Be an active voice to recommend solutions that will exploit opportunities and address critical deficiencies. Will also work closely with business lines and leadership across Wells Fargo in the execution of this strategy.

Senior Lead AI Transformation, Product Strategy

AI Research Scientist

• Train, evaluate, and deploy machine learning models tasked with scoring short answer and essay student responses to formative and summative test administrations from school districts nationwide
• Monitor performance of deployed machine learning models to ensure consistent, fair, and unbiased scoring in real time and recalibrate deployed models as needed
• Maintain, update, and improve code base used to train and deploy machine learning models
• Evaluate historical model performance and conduct experiments exploring strategies to potentially improve team modeling techniques and approaches
• Research and stay up-to-date on emerging technologies in the NLP space
• Be a thought leader in automated scoring by attending and presenting at conferences

Advanced Specialist, AI Scientist

• Design, build, and deploy AI-powered features within our SaaS platform
• Integrate LLMs and cloud AI services into production applications
• Develop scalable backend services, REST APIs, and application integrations
• Build and maintain data pipelines that support AI workflows
• Contribute across the application stack, including backend services and frontend enhancements as needed
• Collaborate with Product Managers, UX Designers, and Software Engineers to deliver customer-centric solutions
• Rapidly prototype new AI capabilities and iterate on customer feedback
• Write clean, maintainable, well-tested code that follows software engineering best practices
• Optimize application performance, scalability, and reliability
• Participate in code reviews and contribute to architectural decisions

AI Software Engineer II

• Design, develop, and deploy machine learning models and AI-powered applications
• Collaborate with cross-functional teams to identify and implement AI/ML solutions for client needs
• Build and maintain data pipelines, model training workflows, and inference systems
• Evaluate and integrate large language models (LLMs), generative AI tools, and emerging AI frameworks
• Monitor model performance, conduct testing, and iterate on improvements
• Document technical architectures, methodologies, and best practices
• Stay current with AI/ML research and translate advancements into practical applications

• Define and evolve enterprise AI architecture, standards, reference models and implementation guidelines to support consistent solution delivery across business functions
• Align AI initiatives to enterprise strategies, roadmaps, and business priorities
• Identify and prioritize AI opportunities that drive student success and operational efficiency
• Design scalable AI platforms and solutions across data, application, and technology domains
• Provide guidance on how AI, ML, and automation will improve current and future-state architectures
• Lead design and implementation of AI-powered solutions (e.g., LLMs, agents, automation, and analytics)
• Integrate AI capabilities with enterprise applications and data platforms
• Guide evaluation and selection of AI/ML models, frameworks, and vendors
• Establish reusable patterns, APIs, and integration approaches
• Establish governance practices covering model oversight, responsible AI usage, security controls, compliance expectations, and lifecycle management
• Establish AI governance, including responsible AI, security, privacy, and compliance (FERPA, HIPAA, GDPR)
• Establish lifecycle management practices (MLOps/LLMOps, model monitoring, drift detection, retraining)
• Implement performance, bias, and risk monitoring frameworks
• Ensure secure AI architecture (e.g., data protection, isolation, adversarial defense)
• Partner with business, product, data, and engineering teams to provide AI solutions
• Provide architectural guidance to architects and delivery teams
• Facilitate workshops, POCs, demos and AI adoption projects identifying viable opportunities where AI can be a positive differentiator
• Mentor teams on AI best practices, patterns, and frameworks

Enterprise AI Architect

• Build, deploy, and maintain production-grade AI and ML pipelines, backend services, and infrastructure that support Oura's AI Advisor and custom model initiatives
• Design and implement evaluation systems and tooling to measure grounding, reliability, and actionability of LLM and agentic systems in production
• Develop and deploy agentic AI systems in production
• Lead initiatives end to end — from problem definition and research through deployment, monitoring, and iteration
• Partner with Product and Engineering teams to translate data-driven insights into shipped product features
• Build and optimize custom AI solutions through fine-tuning, knowledge distillation, and quantization
• Help build Oura as a leader in AI expertise and infrastructure
• Proactively identify opportunities to improve the product and bring your own ideas to the table

Senior AI Scientist

• Lead end-to-end research projects in Agentic AI / AI / Generative AI / Machine Learning from ideation, research, experimentation, implementation, evaluation, transfer and documentation.
• Contribute and lead building the next generation of foundation models and agentic AI systems trained / operating on large volumes of heterogeneous data - both structured and unstructured - that enhance Wells Fargo's products, services, and operations.
• Lead the deployment of foundation models, LLMs, and agentic AI systems into production, with a strong understanding of deployment trade-offs.
• Drive the exploration, application, and rigorous evaluation of emerging AI technologies that solve real-world challenges.
• Collaborate closely with interdisciplinary teams, including researchers, data scientists, applied engineers, and domain experts.
• Translate research insights into impactful business solutions, open-source contributions, patents, and publications.
• Publish in top-tier AI/ML conferences and journals and represent Wells Fargo in the broader AI research community.

Senior Lead AI Research Scientist – Foundation Models, Agentic AI

Machine Learning Engineer

• Design and implement data-driven systems that enhance decision-making across dealer workflows
• Develop predictive or optimization logic using structured dealership data
• Translate business objectives into scalable intelligent services
• Improve consistency, accuracy, and efficiency in operational processes
• Ensure solutions are explainable, reliable, and aligned with enterprise and regulatory expectations
• Build systems that extract insights from structured dealership data
• Implement knowledge retrieval or search capabilities across policies and documentation where appropriate
• Collaborate with product teams to surface actionable intelligence within dashboards and workflows
• Design and deploy containerized AI services within AWS (ECS/Fargate)
• Integrate intelligent services into existing APIs and application workflows
• Monitor system performance, reliability, and integrity
• Ensure data security, auditability, and compliance alignment

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