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

About the role

Ten-week Charlotte summer internship for master’s students applying quantitative analytics, artificial intelligence, machine learning, and advanced computational intelligence to financial-services challenges. Interns gain hands-on project experience, mentorship, technical training, senior-leader exposure, and networking while developing scalable solutions supporting business strategy, risk management, customer experience, and operational efficiency.

Responsibilities and Projects

• Develop AI-powered advisors and decision-support systems using customer, market, relationship, and enterprise data.
• Build generative AI assistants and intelligent agents using enterprise knowledge, reasoning, and workflow orchestration.
• Design agentic and multi-agent systems automating customer service, operational, and business processes.
• Create knowledge-intelligence platforms using RAG, LLMs, multimodal AI, and structured and unstructured data.
• Train, evaluate, optimize, and deploy LLMs, speech technologies, and emerging foundation models.
• Apply statistical and quantitative techniques to validate model design, calibration, and implementation.

Program Experience

• Complete an intern induction week at an offsite location and participate in structured onboarding.
• Receive mentorship, technical training, professional development, and exposure to Wells Fargo senior leaders.
• Network and collaborate with peers while contributing to strategic business goals through on-the-job experiences.
• Develop technical capabilities, business acumen, and leadership skills in a collaborative environment.
• Work in Charlotte, North Carolina, during the June–August 2027 summer program.
• High-performing interns may receive consideration for full-time roles after graduation.

Technical Focus

• Deploy scalable generative AI and machine learning solutions supporting productivity, customer experience, risk management, and decision-making.
• Explore supervised fine-tuning, post-training methodologies, cloud deployment, agent orchestration, RAG, and intelligent-agent deployment.
• Apply distributed GPU training and efficient model-tuning approaches, including LoRA and PEFT.