Senior Quant Developer

Tasks

The role involves developing and maintaining Python data pipelines and analytics platforms for discretionary investment teams and solutions funds. Responsibilities include:

• Build, extend, and maintain Python data pipelines using Pandas, NumPy, and internal libraries
• Develop and maintain production-grade code to support trading, risk, and fund data platforms
• Contribute to AI-powered tools for investment teams—research databases with vector search, and AI integrations for portfolio managers and analysts
• Audit and monitor (fault-tolerant/resilient) backends using shared component libraries
• Collaborate with data engineers and software engineers on cross-team initiatives

Requirements

The position requires:

• 5+ years of professional software engineering experience with a proven track record of successful project delivery
• Proficient in Python: well-versed, capable of writing production-quality code. Comfortable working with dataframes, type hints, and OOP design patterns
• Practical experience in driving delivery and operating at scale
• UML 3 tooling - experience building or integrating LUM-based tools
• A strong software delivery mindset—you collaborate with teams as a matter of course
• Confident in an environment that incorporates ESG, SDLC, penetration testing, and CI/CD
• Strong organizational and prioritization skills—able to consistently handle technical challenges, and capable of reprioritizing and replanning to adapt to changing stakeholder needs as required
• A degree in a STEM field or equivalent practical experience

We offer

The position offers:

• The opportunity to contribute to the development of AI-driven tools for investment teams
• The opportunity to work with an experienced team
• The opportunity to grow in a dynamic and innovative environment

technology stack

The technology stack includes:

• Python (primary), TypeScript/React
• Cloud (AWS, GCP/Aria
• Redis; Apache Kafka, messaging, graph databases
• PostgreSQL, Redis, MongoDB, Airflow
• AI (including Cloud Cohes, LLM agents, vector search, and RAG)