Lead AI Engineer

Wells Fargo is seeking a Lead AI Engineer.

In this role, you will:

• Lead the design, development, and deployment of enterprise-scale AI platforms and GenAI solutions, leveraging LLMs, Agentic AI, RAG, and knowledge retrieval architectures.
• Architect and deliver scalable Python-based applications, APIs, microservices, and data engineering pipelines using cloud-native and distributed processing technologies.
• Establish engineering standards, best practices, and AI/LLMOps frameworks to ensure secure, reliable, and production-ready AI solutions across the organization.
• Collaborate with senior technology leaders, data engineers, architects, and business stakeholders to solve complex technical challenges and drive innovation at scale.
• Mentor engineering teams and lead strategic technology initiatives, accelerating adoption of emerging AI technologies while delivering measurable business value.

• 5+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education.

• Strong software engineering background with 8+ 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.

• 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 semantic search, vector databases (Pinecone, OpenSearch, FAISS), Neo4j knowledge graphs, embeddings, and advanced information retrieval techniques.
• Build secure, scalable, and cloud-native enterprise AI applications including copilots, intelligent assistants, automation platforms, and document intelligence solutions using microservices, APIs, AI/LLMOps practices, CI/CD pipelines, and production deployment frameworks.