AI Engineer

• Build production AI platform capabilities that transform Prologis building, project, asset, and operational data into reusable solutions for construction, procurement, and operations teams.
• Design and implement production AI platform capabilities, including agents, retrieval-augmented generation, tool calling, workflow orchestration, evaluation, and human review.
• Build an AI-native building knowledge platform that links building models, assets, documents, telemetry, and operational context.
• Develop multimodal data pipelines that convert BIM, Autodesk, geospatial, document, and operational data into structured, queryable knowledge.
• Deliver AI workflows supporting design, construction, commissioning, building operations, sustainment, and reinvestment use cases.
• Move priority solutions from prototype through pilot and production with appropriate governance, documentation, evaluation, observability, and operational readiness.
• Establish reusable engineering patterns that enable successful pilots to scale into enterprise platform capabilities.
• Partner with business, technology, security, and operations teams to define workflows, technical scope, implementation plans, rollout approaches, and post-launch improvements.

Lead AI Engineer

• Act as a trusted advisor to leadership, guiding the development and influence of applications, networks, information security, databases, operating systems, web technologies, and emerging AI capabilities to address highly complex business and technical needs across multiple groups
• Lead the strategy and resolution of highly complex, unique challenges through end‑to‑end solution design, including Generative and Agentic AI systems, cloud‑native platforms, and legacy modernization efforts that require long‑term vision, creativity, innovation, and advanced analytical thinking
• Translate advanced technology expertise, deep understanding of enterprise business objectives, organizational structure, and the broader technology ecosystem into scalable engineering solutions, such as reusable architectures, AI-enabled platforms, and production‑ready systems
• Provide vision, direction, and technical leadership to senior leaders on implementing innovative business solutions, embedding AI and automation into product delivery workflows, decision support, and developer productivity while ensuring alignment with security, compliance, and responsible AI standards
• Maintain deep awareness of industry best practices, emerging technologies, and AI advancements, proactively recommending innovations that modernize operations, improve efficiency and quality, and create competitive advantage for the organization
• Strategically engage with professionals and managers at all levels of the enterprise, serving as an expert technical advisor and escalation point for complex platform, data, and AI decisions while mobilizing and guiding large‑scale, cross‑organizational initiatives and communicating progress to executive leadership

Principal AI Engineer

• Develop and maintain relationships with homebuyers, Realtors, builders, financial advisors, and referral partners.
• Guide borrowers through the mortgage process from application through closing.
• Analyze financial information and recommend appropriate loan solutions.
• Build and manage a consistent referral pipeline through networking and relationship development.
• Deliver exceptional customer service throughout the lending process.
• Maintain compliance with all federal, state, and company lending guidelines.
• Grow a sustainable book of business through outstanding client experiences and referral relationships.

Licensed Mortgage Loan Originator

• 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

• Own stakeholder relationships across Medbridge’s executive team, working with them to find the problems and AI enabled solutions that can expedite, or remove, the current blockers in the business.
• Design, build, and ship AI-powered features across our internal teams, from the user interface to the backend services and AI workflows behind it.
• Build full-stack applications using AI-native engineering workflows and tools such as Claude, Cursor, Codex, and OpenAI and Anthropic APIs.
• Develop APIs, backend services, and automation pipelines that support intelligent product capabilities and production-ready deployments.
• Apply software engineering, DevOps, and MLOps best practices to the testing, deployment, monitoring, evaluation, and optimization of AI solutions.
• Partner closely with our Product and Engineering teams to turn ambiguous goals into scalable internal products.
• Use AI tooling throughout your workflow (coding, code review, testing, and planning) and help push the team’s adoption forward.
• Research emerging AI technologies, LLM implementation patterns, and modern product engineering frameworks, and bring what works back to the team.
• Instrument what you ship and use usage data and evidence to decide what to build, fix, or kill next.

Product Engineer – AI

• Define and execute the vision, strategy, and roadmap for AI, automation, and intelligent workflows across HR
• Identify opportunities to improve employee experiences, decision-making, and operational effectiveness through AI-enabled solutions
• Assess current-state capabilities, design future-state experiences, and develop strategies that accelerate transformation
• Translate enterprise priorities into scalable products, platforms, and capabilities that create value across HR
• Drive the development of reusable AI, automation, orchestration, and workflow capabilities that support enterprise-wide adoption
• Establish product strategies that leverage vendor platforms while identifying opportunities for differentiated, custom-built solutions
• Evaluate emerging technologies, AI platforms, automation tools, and data capabilities to determine optimal solutions and investments
• Own product vision, roadmap prioritization, business cases, and value realization, delivering measurable business outcomes
• Partner across HR, Technology, Engineering, Architecture, and Data Science to deliver innovative, user-centric solutions and drive adoption
• Establish governance, operating models, success metrics, and responsible AI practices that balance innovation, risk, compliance, and sustainable growth

Senior Lead Product Manager – Agentic AI, Automation

AI Research Scientist

• Contribute to the research, design, and development of large-scale foundation models for machine-generated data
• Develop and enhance distributed training and inference workflows
• Collaborate with engineering, product, and data science teams to understand requirements
• Share emerging ideas, technical insights, and best practices with teammates
• Take ownership of assigned projects and deliver high-quality results

Applied AI Scientist

• 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

• Lead a cross-functional product pod of Solution Consultants, Pega Configurators, and Business Analysts to deliver Pega-based workflow automation solutions aligned to business priorities.
• Provide day-to-day product direction, delivery oversight, mentorship, and issue resolution support to help the team meet commitments and maintain high execution standards.
• Own solution delivery from discovery and design through configuration, testing, deployment, and post-implementation feedback, with a focus on quality, usability, scalability, and control readiness.
• Work hands-on within Pega App Studio and Dev Studio to support solution design, configuration, testing, and implementation of workflow capabilities.
• Partner with architecture and engineering teams to ensure solutions align with platform architecture, technical standards, security requirements, and software delivery lifecycle expectations.
• Translate operational pain points, process inefficiencies, and business needs into clear product requirements, workflow designs, user stories, acceptance criteria, and delivery plans.
• Partner directly with operational leaders and business stakeholders to assess opportunities, shape solution options, manage expectations, and drive alignment on scope, priorities, benefits, and delivery timelines.
• Establish and monitor success measures for delivered solutions, including adoption, operational efficiency, user experience, quality, risk reduction, and other relevant business outcomes.
• Elevate product management practices across the team by improving intake, prioritization, roadmap planning, stakeholder communications, and outcome measurement.
• Identify and help resolve risks, issues, impediments, and dependencies that could impact delivery quality, control effectiveness, or business outcomes.
• Foster a collaborative, inclusive, and accountable team environment that supports talent development, clear communication, continuous improvement, and operational excellence.

Pega Product Delivery Lead – AI Case Management

• Build and maintain shared infrastructure: secure web app hosting infrastructure, MCP/API standards, secret/key management, model selection, guardrails, and a centralized pattern library that all builders across the organization can pull from.
• Own the review, tracking, and advisory process for all corporate AI and automation builds — MCP servers and tool configurations, Claude Skills, API integrations, and agentic workflows — before deployment.
• Partner directly with AI specialists across departments to identify, scope, and build AI and automation solutions that meet high standards for cost, security, and scalability security before they ship.
• Define and maintain the secure intake path for AI and automation requests across the organization, replacing ad-hoc builds and shadow IT with a governed, cost-effective, and scalable process.
• Contribute to the organization’s AI cost efficiency by monitoring, alerting, and setting thresholds on AI spend across usage-based tools and platforms.

AI & Automations Lead

• Design, prototype, and deliver AI-driven workflows, agents, copilots, and automations using large language models (LLMs) and enterprise AI services.
• Integrate AI capabilities with enterprise platforms and systems (e.g., ServiceNow, Salesforce, data platforms, internal services) using secure APIs and orchestration patterns.
• Rapidly iterate on prototypes and transition them into production-ready solutions that meet enterprise standards for reliability, scalability, and supportability.
• Act as a technical bridge between business, product, data, security, and engineering teams to ensure solutions are usable, compliant, and aligned with business objectives.
• Lead solution architecture and design activities, including: Prompt engineering and AI workflow design including: API integration and service orchestration Enterprise knowledge and data integration Security, privacy, risk, and governance considerations
• Own solutions across the full lifecycle—from concept and proof of value through production deployment and continuous improvement.
• Apply and promote best practices for responsible AI, including model risk management, data protection, and compliance with enterprise and regulatory requirements.
• Contribute to the development of reusable patterns, standards, and guidance to support scalable AI adoption across the enterprise.

Principal AI Engineer, Forward Deployed – CB AI/ML

• 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

• Build evaluation benchmarks and metrics
• Build and iterate on agent harness, including context engineering, agent memory, tools, skills
• Develop robust, reproducible training workflows from data ingestion and preprocessing through model checkpointing and deployment
• Design RL environments and reward functions
• Debug and optimize training runs
• Conduct training runs to improve model capabilities for agentic applications

• Engage in relevant research and development (R&D) of AI systems, models, and advanced machine learning algorithms that augment physics-driven modeling and simulation systems
• Explore and implement AI-powered simulation tooling in support of AI workflows through reinforcement learning, multi-agent systems, and hybrid modeling approaches
• Collaborate with research, engineering, and product teams to build AI-powered solutions that meet mission-critical modeling and decision-support needs
• Engage in and support the drafting and review of conference and journal articles and presentations, sharing advances with both internal stakeholders and the wider research community.
• Contribute content to technical invention disclosures, including associated narrative, graphics, and engagements in support of patent development
• Perform additional responsibilities (no more than 10% of duties) in support of the company’s technology and product development initiatives.

AI Research Scientist, Applied AI

• Responsible for architecting solutions achieving customer business outcomes
• Develop and articulate compelling, accurate, and relevant proposals
• Provide technical expertise to sales teams and customers
• Develop customer proposals and critically review them
• Quantify the impact of business problems and position business value
• Communicate how the solution value propositions address customer business needs
• Drive the Account Business Planning process leveraging knowledge of industry trends and customer’s technical environment

AI Architect – Pre-Sales

• Design, build, and deploy AI-powered tools and automations for HR and Talent use cases, including intelligent workflow automations, employee-facing chatbots, document processing solutions, and decision-support systems
• Identify opportunities to improve manual or repetitive HR workflows through scalable AI/ML-driven automation solutions
• Develop integrations between AI solutions and enterprise systems including Workday, ATS platforms, ServiceNow Ticketing, and other internal tools ensuring seamless data flow and user experience
• Partner directly with HR Operations and cross-functional stakeholders to gather requirements, prototype solutions, run pilots, and drive successful adoption
• Continuously monitor and improve deployed tools based on user feedback, operational metrics, and evolving business needs
• Create technical documentation, implementation guides, and operational handoff materials to support long-term maintainability and scalability.

Principal AI Engineer, HR Automation

• Lead the design, development, and deployment of production AI systems end-to-end
• Contribute hands-on to the codebase and set a high bar for engineering quality
• Establish scalable patterns for evaluation, deployment, and system reliability
• Conduct technical design reviews and guide architectural decisions
• Mentor and develop engineers, providing regular coaching and technical guidance
• Support hiring efforts, including candidate evaluation and onboarding
• Partner with cross-functional stakeholders to align priorities and deliver impactful solutions
• Help translate experimentation into stable, production-ready systems

Full Stack AI Engineer

• Design and analyze neuromorphic AI algorithms for robotics, signal processing, control, and learning on edge platforms
• Develop research-quality software artifacts including APIs, kernels, and benchmarking tools that enable collaborative innovation
• Prototype and evaluate approaches under realistic constraints including quantization, sparsity, memory footprint, latency, and energy optimization
• Collaborate with hardware and software engineering teams to translate algorithmic requirements into hardware specifications
• Build experimental pipelines and benchmarks for target use cases with comprehensive baseline and ablation studies
• Communicate results through technical documentation, customer prototypes, and publications in leading conferences and journals
• Contribute to technical thought leadership in neuromorphic computing and present findings to internal and external stakeholders

Neuromorphic/AI Research Scientist

• This candidate will be configuring data pipelines, establishing data ingestion processes, and implementing predictive modeling components that analyze five (or more) years of historical data to generate time- and location-based risk forecasts.
• Role includes integration with existing systems and data to ensure seamless data flow and usability within current operational workflows.
• Experience with all AI models to validate data integrity, model performance, and system reliability.
• Assessing prediction accuracy, ensuring alignment between forecasted and observed patterns, and verifying that outputs are operationally meaningful for command staff and field leadership.
• Experience within STRIDE platform.
• Build machine learning and deep learning models (e.g., neural networks, LLMs) to address specific business problems.
• Fine-tune AI models to ensure high performance, scalability, and efficiency in cloud environments.

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