Backend Software Engineer
Offer summary
Qualifications:
Proficiency in Python programming., Experience with Event-Driven Architectures., Familiarity with Docker, Kubernetes, and cloud platforms like Azure, AWS, GCP., Interest in learning new technologies and reviewing academic research papers..Key responsibilities:
- Develop and improve data generation pipelines for pseudo-random pricing requests.
- Optimize cluster compute resource usage and data storage strategies.
- Work on microservice architecture to enhance data management processes.
- Collaborate with teams to implement features that increase efficiency and performance.
Job description
Company Description
Founded and managed by Capital Markets industry veterans, Riskfuel is one of the world’s most innovative fintechs. We have pioneered the use of Deep Learning to improve the computational performance of algorithms used by banks and insurance companies by many orders of magnitude. We use state of the art hardware such as NVIDIA DGX A100 and all the major Clouds. See more at our website Riskfuel.com.
Job Description
Riskfuel has a unique set of challenges around Data Management. We use a variety of strategies to generate pseudorandom 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 pseudorandom pricing request generation, more efficient with cluster compute resources, while improving our overall data storage and management strategies.
Qualifications
If you have experience working with EventDriven 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:
- Python
- Docker
- Kubernetes
- Apache PulsarKafka
- Redis
- PyTorch
- Prometheus
- Grafana
- Azure, AWS, GCP
- ArgoCD
- Alluxio
Founded and managed by Capital Markets industry veterans, Riskfuel is one of the world’s most innovative fintechs. We have pioneered the use of Deep Learning to improve the computational performance of algorithms used by banks and insurance companies by many orders of magnitude. We use state of the art hardware such as NVIDIA DGX A100 and all the major Clouds. See more at our website Riskfuel.com.
Job Description
Riskfuel has a unique set of challenges around Data Management. We use a variety of strategies to generate pseudorandom 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 pseudorandom pricing request generation, more efficient with cluster compute resources, while improving our overall data storage and management strategies.
Qualifications
If you have experience working with EventDriven 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:
- Python
- Docker
- Kubernetes
- Apache PulsarKafka
- Redis
- PyTorch
- Prometheus
- Grafana
- Azure, AWS, GCP
- ArgoCD
- Alluxio
Riskfuel has a unique set of challenges around Data Management. We use a variety of strategies to generate pseudorandom 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 pseudorandom pricing request generation, more efficient with cluster compute resources, while improving our overall data storage and management strategies.
Qualifications
If you have experience working with EventDriven 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:
- Python
- Docker
- Kubernetes
- Apache PulsarKafka
- Redis
- PyTorch
- Prometheus
- Grafana
- Azure, AWS, GCP
- ArgoCD
- Alluxio
If you have experience working with EventDriven 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:
- Python
- Docker
- Kubernetes
- Apache PulsarKafka
- Redis
- PyTorch
- Prometheus
- Grafana
- Azure, AWS, GCP
- ArgoCD
- Alluxio
Required profile
Experience
Other Skills
- Teamwork
- Problem Solving
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