Software Engineer - AI Engineer

• About this role:

Wells Fargo is seeking a

Software Engineer - AI Engineer.

In this role, you will:

• 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.

• Required Qualifications:

• 2+ years of software engineering experience, or equivalent demonstrated through or a combination of the following: work experience, training, military experience, education

Desired Qualifications:

• 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

.

Job Expectations:

• 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.

• Posting End Date:

23 Sep 2026

****Job posting may come down early due to volume of applicants.*

We Value Equal Opportunity

Wells Fargo is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.

Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit’s risk appetite and all risk and compliance program requirements.

Candidates applying to job openings posted in Canada: Applications for employment are encouraged from all qualified candidates, including women, persons with disabilities, aboriginal peoples and visible minorities. Accommodation for applicants with disabilities is available upon request in connection with the recruitment process.

Applicants with Disabilities

To request a medical accommodation during the application or interview process, visit Disability Inclusion at Wells Fargo.

Drug and Alcohol Policy

Wells Fargo maintains a drug free workplace. Please see our Drug and Alcohol Policy to learn more.

Wells Fargo Recruitment and Hiring Requirements:**

a. Third-Party recordings are prohibited unless authorized by Wells Fargo.

b. Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process.

• 2+ years of software engineering experience, or equivalent demonstrated through 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-orie
• 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 pro
• 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-Augmen
• Experience developing and supporting enterprise AI applications, including AI assistants, intelligent chatbots, knowledge retrieval platforms, document intelligence solutions, and AI-driven automation
• Good understanding of knowledge management and search architectures, including vector databases and retrieval technologies such as Pinecone, OpenSearch, FAISS, ChromaDB, Neo4j knowledge graphs, semant
• 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 fra