Software Engineer - AI Engineer

Wells Fargo is seeking a Software Engineer - AI Engineer.

• Design, develop, and deploy enterprise AI applications and GenAI solutions leveraging LLMs, Agentic AI, RAG, and knowledge retrieval architectures.
• Develop scalable Python-based applications, APIs, microservices, and data engineering pipelines using cloud-native and distributed processing technologies.
• Contribute to engineering standards, best practices, and AI/LLMOps frameworks to ensure secure, reliable, and production-ready AI solutions.
• Collaborate with technology leaders, data engineers, architects, and business stakeholders to solve technical challenges and deliver innovative solutions.
• Support AI adoption initiatives and contribute to the development of reusable frameworks, accelerators, and enterprise AI capabilities.

• 2+ 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 3-6 years of experience developing and delivering enterprise-scale applications, platforms, and distributed systems, with proficiency in Python, object-oriented design, design patterns, asynchronous programming, API development, automation, performance tuning, and debugging.
• Experience in data engineering and large-scale data processing, including the design and implementation of ETL/ELT pipelines, data ingestion frameworks, orchestration solutions, and transformation processes using technologies such as Spark, Pandas, Databricks, and other distributed computing platforms.
• Hands-on experience building Generative AI solutions, with at least 1-3 years of experience focused on LLM-based applications, utilizing GPT and enterprise LLMs, LangChain, LangGraph, Retrieval-Augmented Generation (RAG), prompt engineering, embeddings, and semantic retrieval techniques.
• Experience developing and supporting enterprise AI applications, including AI assistants, intelligent chatbots, knowledge retrieval platforms, document intelligence solutions, and AI-driven automation capabilities.
• Good 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.
• Experience building REST APIs, microservices, and cloud-native applications leveraging platforms such as GCP and associated AI/ML services.
• Knowledge of database technologies, including SQL, NoSQL, and graph databases, data modeling, and enterprise data architecture principles.
• 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 strong 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, and manage structured and unstructured datasets.
• Develop and enhance Generative AI solutions using Large Language Models (LLMs), Agentic AI, LangChain, LangGraph, and Retrieval-Augmented Generation (RAG) frameworks to solve business challenges and improve productivity.
• Build and optimize knowledge retrieval platforms through semantic search, vector databases (Pinecone, OpenSearch, FAISS), Neo4j knowledge graphs, embeddings, and advanced information retrieval techniques.
• Develop 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.
• Collaborate effectively with cross-functional teams, participate in code reviews, contribute to architectural discussions, and support the delivery of high-quality AI solutions from development through production deployment.
• Experience Profile:
• Total Experience: 3-6 Years
• AI / GenAI Experience: 1-3 Years
• Level: Software Engineer / AI Engineer
• Focus Areas: Python Development, Data Engineering, Generative AI, RAG, Agentic AI, APIs, Cloud-Native Development, Knowledge Retrieval Systems, AI/LLMOps.