Lead AI Engineer

About This Role

• Wells Fargo is seeking a Lead Software Engineer.

In this role, you will:

• Lead complex technology initiatives including those that are companywide with broad impact
• Act as a key participant in developing standards and companywide best practices for engineering complex and large-scale technology solutions for technology engineering disciplines
• Design, code, test, debug, and document for projects and programs
• Review and analyze complex, large-scale technology solutions for tactical and strategic business objectives, enterprise technological environment, and technical challenges that require in-depth evaluation of multiple factors, including intangibles or unprecedented technical factors
• Make decisions in developing standard and companywide best practices for engineering and technology solutions requiring understanding of industry best practices and new technologies, influencing and leading technology team to meet deliverables and drive new initiatives
• Collaborate and consult with key technical experts, senior technology team, and external industry groups to resolve complex technical issues and achieve goals
• Lead projects, teams, or serve as a peer mentor

Required Qualifications:

• 5+ years of Software Engineering experience, with strong expertise in designing and delivering enterprise-scale applications and platforms, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
• Bachelor’s or master’s degree in computer science, Artificial Intelligence, Data Science, Engineering, or a related field.

Desired Qualifications

• Strong software engineering background with 5+ years of experience developing and delivering enterprise-scale applications, platforms, and distributed systems, with expert proficiency in Python, object-oriented design, design patterns, asynchronous programming, API development, automation, performance tuning, and debugging.
• Extensive experience in data engineering and large-scale data processing, including the design and implementation of scalable ETL/ELT pipelines, data ingestion frameworks, orchestration solutions, and transformation processes using technologies such as Spark, Pandas, Databricks, and other distributed computing platforms.
• Proven hands-on experience building production-grade Generative AI solutions, with at least 3+ years focused on LLM-based applications, utilizing GPT and enterprise LLMs, LangChain, LangGraph, Retrieval-Augmented Generation (RAG), Agentic AI, multi-agent systems, prompt engineering, embeddings, and semantic retrieval techniques.
• Demonstrated success designing and deploying enterprise AI applications, including AI assistants, copilots, intelligent chatbots, knowledge retrieval platforms, document intelligence systems, and AI-driven automation solutions that deliver measurable business value.
• Deep understanding of knowledge management and search architectures, including vector databases and retrieval technologies such as Pinecone, OpenSearch, FAISS, ChromaDB, Neo4j knowledge graphs, semantic search, NLP, and information retrieval frameworks.
• Strong experience building REST APIs, microservices, and cloud-native applications with scalable, secure architectures leveraging cloud platforms such as GCP and associated AI/ML services.
• Solid expertise in database technologies, including SQL, NoSQL, graph databases, data modeling, and enterprise data architecture principles.
• Strong understanding of modern software engineering practices, including CI/CD, DevOps, automated testing, code reviews, observability, security, Agile methodologies, AI/LLMOps, and production deployment frameworks.

Job Expectations:

• Demonstrate deep expertise in Python development, building scalable, high-performance applications, automation frameworks, APIs, and enterprise-grade solutions using modern software engineering principles.
• Design and implement robust data engineering and ETL pipelines leveraging Databricks, Spark, and distributed processing frameworks to ingest, transform, enrich, and manage large-scale structured and unstructured datasets.
• Architect and deliver Generative AI solutions using Large Language Models (LLMs), Agentic AI, LangChain, LangGraph, and Retrieval-Augmented Generation (RAG) frameworks to solve complex business challenges and enhance developer productivity.
• Develop and optimize knowledge retrieval platforms through se…