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The Team

Discretionary & Client Solutions Technology is ~35 engineers across London, New York, and Sofia, building the platforms that power Man Group's discretionary investment and client facing operations. We operate at high tempo - shipping multiple production releases weekly across portfolio analytics, fund data platforms, client reporting, data pipelines, dashboards, and AI tools.

We are one of the most active AI-adopting engineering teams in Man Group - building LLM-powered agents, research tools with vector search, and AI integrations that our portfolio managers and analysts use daily. You'll have the opportunity to shape how AI is integrated into investment workflows.

The Role

This is an engineering role sitting at the intersection of Man Group's Discretionary (public markets) and Solutions technology teams in New York. You will be the primary NY-based engineer supporting portfolio managers, analysts and quants functions across both business lines.

The role spans Python data pipelines and analytics platforms serving Discretionary investment teams - equities, credit, and alternatives - alongside Python systems powering Solutions' fund analytics, portfolio management, and reporting infrastructure. You'll work hands-on building and operating production systems while acting as the key technical liaison for NY-based stakeholders.

Our Technology Stack

• Python (primary), TypeScript/React
• Data: Pandas, NumPy, Kafka
• Backend: FastAPI, Flask
• Frontend: React, Streamlit dashboards, Tableau
• Infrastructure: Kubernetes, Airflow
• AI tooling: Claude Code, LLM agents, vector search, RAG - we actively build and ship AI tools for our investment teams

Key Responsibilities 

Development & Delivery (70%)

• Build, extend, and maintain Python data pipelines using Pandas, NumPy, and internal libraries
• Develop and enhance portfolio analytics, risk reporting, and fund data platforms
• Contribute to AI-powered tools for investment teams - research databases with vector search, AI integrations for portfolio managers and analysts
• Build and maintain FastAPI and Flask backends and React/TypeScript frontends using our shared component libraries
• Deploy services to Kubernetes; manage Airflow DAGs for scheduled data workloads
• Collaborate with London and Sofia-based engineers on cross-team initiatives

Stakeholder Engagement (20%)

• Gather requirements, translate business needs into technical solutions, and manage expectations
• Work directly with portfolio managers, analysts, and quants - understanding their workflows is as important as writing the code

Production Operations (10%)

• Own production stability for systems in your portfolio - investigate data quality issues, triage support requests, manage incident response
• Maintain documentation and operational processes

Key Competencies 

Essential

• 2+ years of professional software engineering experience with a strong delivery track record
• Strong Python: Well-tested, modular production code. Comfortable with dataclasses, type hints, OOP, design patterns
• Financial services experience - particularly portfolio management, risk, or investment operations
• Pandas/NumPy proficiency: Building efficient data pipelines and transformations at scale
• AI/LLM tooling - experience building with or integrating LLM-based tools. We're active early adopters of AI-assisted development and are building AI agents for our investment teams
• Solid testing discipline: pytest, unit tests, integration tests - you write tests as a matter of course
• Comfortable in a Linux environment with Git, virtual environments, and CLI tooling
• Strong communicator: You can explain technical decisions to non-technical stakeholders concisely, and you view stakeholder interaction as integral to good engineering - not overhead
• Self-directed: You manage your own priorities, flag blockers early, and drive work to completion with appropriate autonomy
• Degree in a STEM subject or equivalent practical experience

Advantageous  

• React/TypeScript for frontend development
• Experience with web APIs: Building and consuming RESTful services (FastAPI, Flask, or equivalent)
• C#/.NET familiarity - some Solutions systems are built in .NET. You won't be the primary .NET engineer, but being able to read and navigate the codebase is valuable
• Kubernetes & containerisation - deploying and troubleshooting services in k8s
• Airflow - building and maintaining scheduled data pipelines
• Distributed systems concepts - messaging architectures, event-driven design, caching strategies
• Experience working with geographically distributed teams - London will be your primary collaboration hub