About this role:
Wells Fargo is seeking a Senior Artificial Intelligence Solutions Consultant to support the implementation of generative AI capabilities for data, analytics, and decision sciences. This role combines hands-on technical skills with project-level consulting to help decision science teams refine use cases, develop and test prototypes, and responsibly use approved Gen AI tools and automation. The consultant will work within defined objectives, collaborate with immediate team members and business partners, and contribute reusable components and documentation that improve analytics and modeling workflows while following applicable quality, risk, and governance requirements.
In this role, you will:
Contribute as an individual consultant to moderately complex Gen AI initiatives for data, analytics, modeling, and decision science teamsCollaborate with decision science practitioners and business partners to refine Gen AI and automation use cases based on defined business needs, technical feasibility, data readiness, risk, and adoption considerationsDevelop and test Gen AI prototypes that improve analytical workflows, including data discovery and preparation, code generation and review, exploratory analysis, model development support, documentation, monitoring, knowledge retrieval, and repeatable reportingApply prompt engineering, retrieval-augmented generation, agentic workflow, semantic retrieval, evaluation, and human-in-the-loop techniques using approved tools and established patternsTranslate defined business and analytical requirements into technical tasks, prototype approaches, test plans, success measures, and implementation activities with guidance from more experienced team membersBuild and maintain reusable prompts, evaluation scripts, automation components, and technical documentation for use within assigned projects or workstreamsParticipate in technical working sessions, demonstrations, peer reviews, and knowledge-sharing activities with data scientists, analysts, model developers, product partners, and control partnersPerform structured testing and evaluation of solution outputs for accuracy, relevance, robustness, traceability, security, responsible AI considerations, and readiness for the intended useWork with immediate business, data, technology, model risk, compliance, and control partners to apply established standards and complete required review and governance activitiesAnalyze technical issues, document options and tradeoffs, escalate decisions outside assigned authority, and communicate progress and recommendations clearly to project leads and business partnersRequired Qualifications:
3+ years of Artificial Intelligence Solutions, data science, advanced analytics, or related consulting experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, educationExperience delivering assigned components of moderately complex projects, working within defined objectives, and coordinating effectively with cross-functional team membersHands-on experience applying Generative AI or large language models to data, analytics, modeling, or decision science workflowsProficiency in Python and SQL, including data processing, API integration, testing, debugging, and workflow automationExperience with at least one of the following: prompt engineering, retrieval-augmented generation, agentic workflows, semantic retrieval, structured outputs, or LLM evaluation methodsExperience working with analytical data platforms and frameworks such as Pandas, Spark, Databricks, cloud-based AI/ML services, or equivalent technologiesExperience translating defined business and analytical problems into technical requirements, prototypes, test results, and implementation recommendationsStrong communication and collaboration skills, including requirements clarification, technical documentation, working-session participation, and the ability to explain technical concepts to immediate team members and business partnersFoundational understanding of responsible AI, model and data governance, information security, privacy, quality, testing, and human oversight considerations relevant to enterprise AI solutionsDesired Qualifications:
Candidates are not expected to have experience in every technology listed below. Strong candidates will typically bring hands-on experience in several of these areas, along with the ability to learn and apply additional tools and patterns on the job.
Experience supporting data science, quantitative analytics, model development, marketing analytics, or decision intelligence teamsExperience automating components of analytics or model-lifecycle activities such as data preparation, code migration, model documentation, validation support, monitoring, forecasting, campaign analytics, or insight generationHands-on experience with enterprise AI assistants and development environments such as Microsoft 365 Copilot, Copilot Studio, GitHub Copilot, approved low-code/no-code AI platforms, or comparable toolsFamiliarity with orchestration frameworks such as LangChain or LangGraph and with enterprise LLM, RAG, or agentic solution patternsFamiliarity with vector databases, semantic retrieval, metadata management, knowledge graphs, or semantic layers that improve LLM grounding in enterprise data and analytical contextExperience using or supporting REST APIs, lightweight services, reusable Python modules, and cloud-native components for analytics automationFamiliarity with AI/LLM Ops practices, including versioning, evaluation, observability, testing, deployment controls, and performance monitoringExperience with one or more additional capabilities such as knowledge graphs, semantic or metadata layers, advanced RAG or agentic implementation, cloud AI/ML integration, or production deployment practicesAbility to contribute to reusable standards, playbooks, technical documentation, training materials, and team knowledge-sharing activitiesCritical Must-Have Skills
Generative AI implementation for data, analytics, and decision science use casesPython, SQL, data processing, testing, and analytics workflow automationHands-on experience in one or more of the following: prompt engineering, RAG, agentic workflows, semantic retrieval, structured outputs, or LLM evaluationTechnical collaboration, requirements clarification, and clear communication with project teams and business partnersPrototyping, component-level solution design, and use of established implementation patternsFoundational awareness of responsible AI, data governance, quality assurance, security, and risk-aware execution
