Lead AI Engineer

About this role: 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 . 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 semantic search, vector databases (Pinecone,