Riskfuel has a unique set of challenges around Data Management. We use a variety of strategies to generate pseudo-random pricing requests based on financial model specifications and distribute 100 million+ pricing request calculations across thousands of nodes in our Kubernetes Clusters to generate a unique trade database for each model. This Trade Database becomes the training data that teaches our Neural Networks to mimic the Bank and Insurance company models and deliver performance up to 1 million times faster.
This role is primarily focused on the Microservice Architecture and data management strategies used in our data generation pipeline. We’re developing features to be smarter around our pseudo-random pricing request generation, more efficient with cluster compute resources, while improving our overall data storage and management strategies.
If you have experience working with Event-Driven Architectures, are proficient with Python, like diving into new technologies, and aren’t afraid to pick up occasional academic research papers, this may be a good role for you.
Our Tech Stack, in order of importance, currently includes:
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Riskfuel is one of the world’s most innovative fintechs, pioneering the use of Deep Learning to improve the computational performance of algorithms used by banks and insurance companies.