Phi Partners is a global Capital Markets IT consultancy specialising in Front Office and Risk Technologies for Tier-1 investment banks, hedge funds, and asset managers. Founded in 2004 and headquartered in London, we operate across North America, EMEA, and APAC, delivering deep technical expertise. Our consultants work directly with trading desks, quantitative teams, and technology stakeholders to deliver projects that are both technically complex and commercially critical.
We are continuing to expand our London presence and Centres of Excellence across Europe and the Americas, with senior engineers leading strategic buildouts focused on low latency eTrading, market connectivity, and modern data platforms. Our programmes often involve enhancing legacy trading and market data systems, improving data integration pipelines, strengthening performance and resilience, and supporting the introduction of new asset classes and functionalities. This environment offers engineers the opportunity to work on high-impact, front-office-aligned initiatives while contributing to modernisation efforts across the broader technology stack.
Our client is a North American Tier-1 investment bank with whom we have maintained a long-standing professional relationship, having supplied over 80 consultants. They are currently undertaking a major transformation of their interest rate trading technology stack, focusing on modernising pricing, risk, and trade booking systems while moving from legacy infrastructure to a Python-based microservices architecture.
The team sits in Pre-Trade Analytics and builds and maintains distributed platforms that price high volumes of trades and produce intraday risk metrics, operating at scale with a focus on performance, parallel processing, and reliability. This role involves working on core trading infrastructure, contributing to platform modernisation, performance improvement, and the development of scalable analytics systems used by global markets teams.
You will work closely with global technology teams, quantitative analysts, and front-office stakeholders to deliver high-performance Python systems.
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