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FactSet creates flexible, open data and software solutions for over 200,000 investment professionals worldwide, provi...

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Selected role

Junior Machine Learning Engineer (Python, Docker, and API development, Cloud Architecture (AWS), AI/ML) - Hybrid

FactSet London, ENG, GB

$200k Senior Full-time

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.

Join the DSAI team at FactSet, where our mission is to enrich data from across the company to enable it to be used in GenAI workflows. Core to the DSAI infrastructure is a knowledge graph that connects financial concepts to the data available at FactSet. Engineers on the team maintain and enhance a GenAI powered software stack that operates at the intersection of financial data, knowledge management, and data engineering.

You will be working on a team in a fast-paced environment where you will have the opportunity to influence the design and architecture of the product. An ideal candidate for the role would be an individual that has experience or a strong interest in working with generative AI and related technologies. They will also have the confidence to meaningfully contribute to team meetings in order to help lead discussions and drive outcomes.

What You'll Do

Build new systems to ingest and enrich data into an ontology.
Monitor and enhance the accuracy, performance, and observability of our GenAI RAG stack.
Evaluate new large language models, tools, and AI engineering techniques.
Improve query planning, optimization, and evaluation infrastructure.
Partner and collaborate with product development leads to identify technical requirements for future product enhancements.
Collaborate with teams across the organization to understand their data.

*What We're Looking For

Required Skills**

2 + years software engineering experience, with a significant portion focused on AI/ML solutions in production environments.
Demonstrated expertise in cloud architecture (AWS) and a range of associated services.
Working knowledge of Natural Language Processing/Machine Learning/Deep Learning concepts and their applications, with a proven track record of successful model development and deployment.
P roficiency in Python, Docker, and API development.
Strong technical writing and presentation skills
Familiarity with relational databases and data modeling techniques.
Bachelor’s degree in computer science, computer engineering, or relevant

Desired Skills

Experience with Cloud platforms such as AWS or Heroku
Experience or knowledge of CI/CD concepts and GitHub
An interest in the financial services domain

Company Overview:

FactSet ( NYSE:FDS | NASDAQ:FDS ) helps the financial community to see more, think bigger, and work better. Our digital platform and enterprise solutions deliver financial data, analytics, and open technology to more than 8,200 global clients, including over 200,000 individual users. Clients across the buy-side and sell-side, as well as wealth managers, private equity firms, and corporations, achieve more every day with our comprehensive and connected content, flexible next-generation workflow solutions, and client-centric specialized support. As a member of the S&P 500, we are committed to sustainable growth and have been recognized among the Best Places to Work in 2023 by Glassdoor as a Glassdoor Employees’ Choice Award winner. Learn more at www.factset.com and follow us on X and LinkedIn .

At FactSet, we celebrate difference of thought, experience, and perspective. Qualified applicants will be considered for employment without regard to characteristics protected by law.

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