Man Group

Software Engineer

New York CityFrom $150kmidAdded today

About this role

Man Group's Direct Lending team seeks a Software Engineer to build and maintain full-stack systems—including data pipelines, analytics platforms, and AI-powered tools—supporting private credit investment operations in New York. You'll work closely with investment professionals and collaborate with the broader UK technology team on a largely greenfield platform with significant opportunities to shape its evolution.

What you'll do

Develop and maintain production systems across data pipelines, analytics platforms, web applications, and AI-driven automation
Collaborate with investment professionals to understand workflows and translate requirements into technical solutions
Build web-based tools and data visualization features for complex financial datasets
Integrate and implement AI/LLM-powered capabilities into private credit workflows
Contribute to full-stack development using Python, Linux, and modern web technologies
Practice strong software engineering fundamentals including code review, testing, CI/CD, and agile methodologies

What they're looking for

Python (strong proficiency)
Linux and shell scripting
Data analysis (NumPy, SciPy, Pandas)
Web development and data visualization
AI/LLM integration and tools
Agile development and collaborative engineering practices
Continuous integration and testing
Full-stack systems design

Benefits

Opportunity to shape a greenfield technology platform with minimal legacy constraints
Work at the forefront of AI adoption within a major investment management firm
Collaborate directly with experienced investment professionals and global tech team
Exposure to private credit and alternative investment workflows
Based in New York with connection to London office

Likely interview questions

Describe your experience building AI or LLM-powered tools—what use cases have you tackled and what challenges did you face?
Walk us through a time you built a data pipeline or analytics system; how did you ensure data quality and performance at scale?

How do you approach learning an unfamiliar investment domain while building technical solutions for domain experts?
Tell us about your experience with Python data libraries like NumPy, Pandas, or SciPy—give a concrete example of how you used them.
What's your experience with web-based visualization of complex, large datasets, and which technologies have you used?
How do you balance greenfield development with maintaining production systems, and what practices do you use to keep code quality high?
Describe your collaborative approach to code review and agile development—how have you contributed to improving team engineering practices?

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