Digital Technology - Undergrad Intern

At Franklin Templeton, we’re advancing our industry forward by developing new and innovative ways to help our clients achieve their investment goals. Our dynamic firm spans asset management, wealth management and fintech, offering many ways to help investors make progress toward their goals. Talented teams working around the globe bring expertise that’s both broad and unique. And our welcoming, respectful and inclusive culture provides opportunities to help you reach your potential while helping our clients reach theirs.

Come join us in delivering better outcomes for our clients around the world!

2026 Undergraduate Summer Intern – Job Posting  

Department Title: Undergrad Intern – Digital Technology 

Position Summary:   

Research, development, and application of AI technologies to support use cases and workflows of FT internal clients. 

• Fine-tuning LLM and deep learning models for various applications 

• Evaluating the quality of third-party AI platforms  

• Performing ad-hoc analyzes of product usage 

• Developing reports detailing model strengths and weaknesses 

• Preparing materials for engagement meetings with stakeholders 

Team Culture: 

Projects within FTT’s Digital Technology AI group are internal to FT and span the entire organization, including its investment managers. The team consists of a wide range of engineers with a variety of skillsets across the application stack, enabling a diverse, supportive, and collaborative engineering environment. The product and research teams are cross functional, comprising multiple areas, including product, engineering, and AI. These teams directly interact with stakeholders from both internal leadership and partner teams, building custom AI-driven solutions. Each unique product offering leverages a variety of datasets following data governance guidelines.   

An intern in this department can expect to learn: 

• Fundamentals and applications of LLM technologies in finance 

• Data modeling and system design for AI/ML applications 

• Understanding the product development lifecycle 

• Building dashboards and reports to detail product usage and adoption 

• Effective communication of methods and results to technical and non-technical stakeholders 

• End-to-end involvement in AI/ML applications from product inception to production 

Key Responsibilities Can Include: 

• Build RAGs, fine-tune LLMs, and engineer prompts for a variety of use cases 

• Evaluate and monitor LLM performance  

• Perform ad-hoc reporting and data analysis 

• Clean and pre-process datasets 

• Contribute to the design and documentation of system architectures 

Ideal Qualifications: 

• Experience developing and debugging in Python (Consuming APIs, data science and