Senior Quant Developer

The role

This is a senior engineering role that bridges Man Group’s Discretionary (public markets) and Solutions Technology teams. You will be responsible for a portion of our entire tech stack from end to end and will be part of the engineering teams that support both business units.

Our technology

Our technology includes:

• Python (primarily), TypeScript/Node
• Data Pipelines, NumPy, Kafka
• Elasticsearch, Neo4j, Pandas
• Internet React, Streamlit Dashboards, TaLib
• Infrastructure Subsystems: Airflow
• A leading cloud data platform, LLM experts, vector search, RAG—we actively build and deliver AI tools for our investment teams

Focus areas of work

The job involves:

• Development & Delivery (70%):
  • Development, expansion, and maintenance of Python data pipelines using Pandas, NumPy, and internal libraries
  • Ownership and maintenance of performance benchmarks, risk research
  • Article on AI-powered tools for investment teams - Research databases with vector search, AI libraries
  • Collaborating with engineers on technology and data to build and operate our trading systems
  • Guide to Kubeflow as a technical service: Managing Airflow DAGs for scheduled and ad hoc workflows
  • Development and maintenance of FastAPIs and Flask backends, as well as the latest/NextGenScript frontends, using our shared component libraries
• Stakeholder engagement (20%):
  • Serve as the primary technical point of contact for New York-based Discretionary and Solutions stakeholders
  • Requirement gathering; translating business requirements into technical solutions and managing expectations
  • Work directly with portfolio managers, analysts, and quants—understanding their workflows is just as important as writing code
• Production Operations (10%):
  • Ensuring operational stability for systems within your portfolio—investigating data quality issues, triaging support requests, and managing incident response
  • Maintenance of Documentation and Operational Processes

Requirements

Essential:

• 5+ years of professional software engineering experience with a strong track record of delivering results
• Strong Python skills: well-tested, modular production code. Proficient with data classes, type annotations, ODP, and design patterns
• Experience with cloud services—particularly portfolio management, risk management, or investment operations
• Product/Technology Knowledge: Building efficient data pipelines and transformations at scale
• AI/LLM experts: Experience in developing and/or integrating LLM-based agents
• Data literacy: a data-driven mindset with strong knowledge of databases; you should be able to interpret data and draw insights from it
• Comfortable working in a Linux environment with the CLI and virtual environments