Lead AI Engineer

About the Role

Key Responsibilities

• Lead the design, development, and deployment of Generative AI solutions using state-of-the-art RAG and Agentic AI Automation techniques.
• Architect and implement RAG pipelines that effectively retrieve, process, and integrate relevant information from diverse data sources to enhance the performance of generative models.
• Design and build Agentic AI systems that can autonomously perform complex tasks, learn from experience, and adapt to changing environments.
• Lead a team of AI engineers, providing technical guidance, mentorship, and support to foster a collaborative and high-performing environment.
• Conduct research and experimentation to identify and evaluate new AI technologies and techniques that can improve the capabilities of our Generative AI solutions.
• Collaborate with product managers, data scientists, and other stakeholders to define project requirements, develop technical specifications, and ensure alignment with business goals.
• Develop and maintain robust testing and validation procedures to ensure the quality, reliability, and security of AI solutions.
• Monitor and analyze the performance of AI systems, identifying areas for improvement and implementing optimization strategies.
• Stay up-to-date with the latest advancements in Generative AI, RAG, and Agentic AI Automation, and share knowledge with the team.
• Contribute to the development of intellectual property through patents, publications, and open-source contributions.
• Communicate complex technical concepts clearly and effectively to both technical and non-technical audiences.

Requirements

• 7+ years of Engineering experience, or equivalent demonstrated through work experience, training, military experience, or education.
• 7+ years of experience in developing and deploying AI/ML solutions in a production environment.
• 3+ years of experience specifically working with Generative AI models (e.g., large language models, diffusion models).
• Strong understanding of RAG techniques and experience building RAG pipelines using tools and frameworks such as LangChain, LlamaIndex, or similar.
• Experience with Agentic AI Automation and building autonomous AI systems.
• Proficiency in programming languages such as Python, with experience in relevant libraries and frameworks (e.g., TensorFlow, PyTorch, Transformers).
• Experience with cloud computing platforms such as AWS, Azure, or GCP.
• Solid understanding of data structures, algorithms, and software engineering principles.
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration skills.
• Experience with version control systems (e.g., Git).

Nice to Have

• Experience with fine-tuning and customizing large language models.
• Experience with deploying AI models using containerization technologies (e.g., Docker, Kubernetes).
• Experience with MLOps practices and tools.
• Experience with building and deploying AI solutions for Financial Services.
• Experience with knowledge graphs and semantic technologies.
• Familiarity with prompt engineering techniques.
• Experience with AI safety and ethical considerations.

Qualifications

• Educational background or formal qualifications are not explicitly specified but experience levels are emphasized.

Benefits & Perks

• Information about Wells Fargo's US employee benefits (http://www.wellsfargo.com/about/careers/benefits/)
• Information about Wells Fargo's International employee benefits
• Health benefits
• 401(k) Plan
• Paid time off
• Disability benefits
• Life insurance, critical illness insurance, and accident insurance
• Parental leave
• Critical caregiving leave
• Discounts and savings
• Commuter benefits
• Tuition reimbursement
• Scholarships for dependent children
• Adoption reimbursement