2027 Quantitative Analytics Program – Applied Computational Intelligence (ACI Masters) – Early Careers

About this role:   Wells Fargo is seeking talent to join the   2027 Quantitative Analytics Program   ACI   (Masters).   Learn more about the career areas and lines of business at   wellsfargojobs.com .    Program Overview |   The Wells Fargo Quantitative Analytics Program offers   Masters   candidates an opportunity to apply advanced analytics, artificial intelligence, and machine learning to complex business challenges at one of the world's leading financial institutions.   This 12-month development program combines hands-on project experience, mentorship, technical training, and exposure to senior leaders. Through two six-month rotations,   you'll   work alongside experienced quantitative professionals, helping develop and evaluate innovative solutions that support business strategy, risk management, and customer experience across Wells Fargo.   You'll   be expected to bring fresh perspectives, explore innovative approaches, and contribute to solutions that support Wells Fargo's strategic priorities. Along the way,   you'll   develop not only your technical capabilities but also the business acumen and leadership skills needed to succeed in a highly collaborative environment.   Upon completion of the program,   you'll   transition into a full-time role aligned with your skills, interests, program   experience,   and business need s .    #earlycareers   As part of the Applied Computational Intelligence track,   you'll   work on   innovative projects   that   leverage   emerging AI technologies and advanced modeling techniques.   Example projects may include:   Develop AI-powered advisors and decision support systems that synthesize customer, relationship, market, and enterprise data to generate insights, recommendations, and actions.   Build Generative AI assistants and intelligent agents that   leverage   enterprise knowledge, reasoning, and workflow orchestration to support employees and customers.   Design and deploy agentic AI and multi-agent systems that automate customer service, operational, and business processes through planning, task execution, and human-in-the-loop collaboration.   Create enterprise knowledge intelligence platforms using Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), multimodal AI, and structured and unstructured data to power search, reasoning, decision support, and workflow automation.   Advance the state of enterprise AI through model training, evaluation, optimization, and deployment of LLMs, speech technologies, and emerging foundation models.   Deploy scalable Generative AI and machine learning solutions that improve productivity, customer experience, risk management, decision-making, and operational efficiency across the enterprise.   Apply statistical and quantitative techniques to   validate   model design, calibration , and implementation .   What   You’ll   Experience:   Develop and deploy AI and machine learning solutions across generative AI, agentic systems, and traditional machine learning applications   Design and build LLM-powered agents and multi-agent systems capable of planning, reasoning, task orchestration, and human-in-the-loop collaboration   Monitor production models and AI systems, evaluating performance, stability, and model drift through testing and analytics frameworks   Apply advanced quantitative techniques to solve real-world business problems   Collaborate with cross-functional teams, technical experts, and business leaders across the organization   Gain exposure to enterprise-scale AI development, governance, and risk management practices   Design, train, fine-tune, and evaluate transformer-based, foundation, and small language models (SLMs).    Apply AI, machine learning, and generative AI techniques to solve complex business problems.    Build and   optimize   scalable model training and deployment pipelines.    Leverage distributed computing and advanced training techniques to improve model performance and efficiency.    Enhance model performance through distillation, quantization, and pruning.    Optimize   inference speed, latency, throughout, and cost for   production of   AI systems.   Program dates:   July 2027 - July 2028       Program Duration :   12 months       Program Location:   Charlotte, NC        Required Qualifications: 6+ months of work experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education Required Qualifications for Europe, Middle East & Africa only: Work experience, or equivalent demonstrated through one or a combination of the following: work experience, training, education Desired Qualifications:   Currently pursuing a   Masters   degree in Computer Science, Machine Learning, Artificial Intelligence, Engineering or related quantitative field or related quantitative field, with an expected graduation date between December 2026 – June 2027.    Technical Skills   Strong programming experience with tools such as Python, R, SQL, Java, Spark, or similar technologies   Hands-on experience developing machine learning and AI solutions in research, academic, or industry environments   Knowledge and Experience In:   Large Language Models & Model Training   Experience with supervised fine-tuning (SFT) and post-training methodologies including RLHF, RLAIF, PPO, DPO, and GRPO   Training and deploying models in cloud environments, including GCP   Agentic AI   Multi-agent architectures and orchestration frameworks like   LangChain ,   LangGraph , Google's ADK,   CrewAI   Retrieval-Augmented Generation (RAG) applications and intelligent agent deployment   AI Infrastructure & Optimization   Distributed GPU training   Efficient model tuning approaches such as   LoRA   and PEFT   Additional Qualifications   Strong quantitative and analytical skills, with the ability to apply data anal