Job Description
About Man Group Man Group is a global alternative investment management firm focused on pursuing outperformance for sophisticated clients via our Systematic, Discretionary and Solutions offerings. Powered by talent and advanced technology, our single and multi-manager investment strategies are underpinned by deep research and span public and private markets, across all major asset classes, with a significant focus on alternatives. Man Group takes a partnership approach to working with clients, establishing deep connections and creating tailored solutions to meet their investment goals and those of the millions of retirees and savers they represent. Headquartered in London, we manage $227.6 billion* and operate across multiple offices globally. Man Group plc is listed on the London Stock Exchange under the ticker EMG.LN and is a constituent of the FTSE 250 Index. Further information can be found at www.man.com As at 31 December 2025 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 a senior 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%) Serve as the primary technical contact for NY-based Discretionary and Solutions stakeholders 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 5+ 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 test