FactSet creates flexible, open data and software solutions for over 200,000 investment professionals worldwide, providing instant access to financial data and analytics that investors use to make crucial decisions.
At FactSet, our values are the foundation of everything we do. They express how we act and operate , serve as a compass in our decision-making, and play a big role in how we treat each other, our clients, and our communities. We believe that the best ideas can come from anyone, anywhere, at any time, and that curiosity is the key to anticipating our clients’ needs and exceeding their expectations.
FactSet’s AI & Automation team works to improve productivity of all employees using advanced AI and automation tools, enabling us to focus on higher value work. You will play a significant role in implementing solutions that benefit your colleagues.

REQUIRED SKILLS:

• 8+ years of software engineering experience, partially focused on LLM/AI/ML solutions.
• Strong hands-on experience with LLM Saas providers (OpenAI, Claude, Gemini).
• Demonstrated expertise and managed services within cloud architecture (primarily AWS).
• Proficient in Python/Typescript/Nodejs, with strong skills in API development.
• Knowledge of major database architectures including SQL, NoSQL, and Vector databases.
• Experience with Knowledge Graphs and architecting LLM-powered solutions.

DESIRED SKILLS:

• Excellent communication skills, capable of engaging both technical and business audiences alike, and leading cross-functional projects.
• Mentor junior team members

EDUCATION:

• Bachelor’s or master’s degree in computer science or equivalent.

• Architect groundbreaking agentic workflows tailored to task automation within inhouse, and 3rd party licensed software service eco-systems.
• Implement agents within frameworks to automate various repetitive manual tasks and summarize data out of corporate tools in concise human readable form.
• Design insight generation techniques that summarize key highlights from large complex datasets and support natural language prompts for deep-dive analysis.
• Construct and maintain robust data pipelines capable of processing complex structured and unstructured quantitative and textual data, ensuring high quality inputs for our models.
• Work closely with data scientists and ML engineers to integrate and manage diverse ML and NLP models within production environments effectively.