Lead AI Engineer

Wells Fargo & Company•Iselin, NJ

About The Position

About this role: The COO Technology group provides technology services for the Chief Operating Office. This includes operations, control executives, strategic execution, business continuity and resiliency, data solutions and services, regulatory relations, customer experience, enterprise shared services, supply chain management, and the corporate properties group. COO Technology provides technology solutions and manages application portfolios for these groups to support modernization and optimization. Within COO Technology we are seeking a highly skilled and motivated Lead Specialty Software Engineer to develop and implement cutting-edge Generative AI solutions. This role will focus on building Agentic AI Automation to build intelligent, innovative, and impactful applications. The ideal candidate will possess a strong background in AI/ML, a deep understanding of generative models, experience in fine tuning LLM models, and proven experience in leading technical projects. You will be responsible for guiding a team of engineers, driving technical direction, and ensuring the successful delivery of high-quality AI solutions. In this role, you will: Design and build state-of-art Agentic AI systems that can autonomously perform complex tasks, learn from experience, and adapt to changing environments Train, fine tune, and evaluate the performance of the LLM-based models to enhance the agentic AI systems Develop and maintain robust testing and validation procedures to ensure the quality, reliability, and security of AI solutions Architect and implement context engineering services that effectively retrieve, process, structure, and integrate relevant information from diverse data sources to enhance the performance of generative models Lead team to ensure compliance and risk management requirements for supported area are met and work with other stakeholders to implement key risk initiatives Mentor less experienced software engineers Collaborate and influence all levels of professionals including managers Partner with production support and platform engineering teams effectively

Requirements

5+ years of Specialty Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
5+ years of experience in Agile AI/ML system development
2+ years of experience in context engineering and LLM technologies, including crafting and optimizing information retrieval from large knowledge base
2+ years of experience using or developing automated LLM or Agentic AI evaluation frameworks on a large scale
1+ years experience with Agentic AI Automation using tools and frameworks such as Lang Graph, Google ADK or similar

Nice To Haves

Bachelor’s or Masters degree in Computer Science, Information Systems, or related field
Proficiency in programming languages such as Python, with experience in relevant libraries and frameworks (e.g., TensorFlow, PyTorch, Transformers)
Expertise in building data recipes and training pipelines for SFT or RL model training for SLMs or LLMs
Experience in fine tuning SLM or LLM models at scale
Experience in evaluation and scoring LLM Chain-of-Thought and agent trajectories
Experience in leveraging MLOps and LLMOps frameworks for observability, evaluation, and human-in-the-look interactions
Experience with GCP cloud computing platform
Experience with knowledge graphs and semantic search
Experience in AI risk assessment, AI safety, and ethical considerations
Experience deploying AI models using containerization technologies (e.g., Docker, Kubernetes)
Excellent communication and presentation skills across technical and non-technical audiences
Knowledge of fraud management tools and techniques
Proven ability to influence and build trust across organizational boundaries

Responsibilities

Design and build state-of-art Agentic AI systems that can autonomously perform complex tasks, learn from experience, and adapt to changing environments
Train, fine tune, and evaluate the performance of the LLM-based models to enhance the agentic AI systems
Develop and maintain robust testing and validation procedures to ensure the quality, reliability, and security of AI solutions
Architect and implement context engineering services that effectively retrieve, process, structure, and integrate relevant information from diverse data sources to enhance the performance of generative models
Lead team to ensure compliance and risk management requirements for supported area are met and work with other stakeholders to implement key risk initiatives
Mentor less experienced software engineers
Collaborate and influence all levels of professionals including managers
Partner with production support and platform engineering teams effectively