Top Engineering Jobs in London

Deliver customer enablement for hyperexponential’s Developer+ programme through workshops, code reviews, drop-in support, and technical guidance. Help customers move from hx-led development to independent delivery, covering architecture, coding practices, testing, deployment, and model lifecycles. Build repeatable delivery processes, convert insights into Academy content, use AI to scale support, and communicate recurring customer feedback to Product.

Staff Product Engineer responsible for setting technical direction across Model Authoring teams, establishing engineering standards, owning shared infrastructure and technical interfaces, and solving complex product problems hands-on. The role requires product focus, architectural leadership in ambiguous environments, cross-team influence, AI-native engineering experience, and mentoring. The engineer will help define trustworthy agentic development practices and build tools supporting actuarial pricing model creation and management.

Own end-to-end delivery of production-grade Python models, from scoping and development through testing, iteration, and customer deployment. Collaborate with actuarial teams and stakeholders to solve complex modeling and coding problems, improve development standards, conduct code reviews, and mentor junior developers. The role applies predictive modeling and advanced analytics in data-heavy environments while requiring ownership of ambiguous problems and direct customer engagement.

Build and maintain Python-based predictive models on the hx modelling platform, collaborate with customer actuarial teams and developers, review and test models, develop best-practice guidance and training materials, and improve model development standards. The role requires production-grade Python, predictive modelling or advanced analytics experience, autonomy, experimentation, and comfort solving ambiguous customer problems.

As a Model Developer, you will deliver end-to-end model solutions using Python, collaborate with actuarial teams, mentor juniors, and improve model development practices.

Lead the technical direction for an AI-first platform: define architecture, standards, and roadmaps; drive platform adoption, security, reliability, and operational excellence; embed AI/automation patterns; influence senior stakeholders; and develop senior engineers to multiply organisational effectiveness.