Job Opportunities API

Imagine working with cutting-edge machine learning models, designing robust and scalable software infrastructure, and collaborating with dedicated colleagues — all while playing a key role in scaling our business even further. Does this sound exciting? Then you might be our new Quant Infrastructure Engineer at Alipes Capital. At Alipes Capital, we have been a market leader in automating financial markets since our inception in 2008, pioneering fully automated natural language processing systems to read and interpret financial news. To accomplish our goal of being the best at what we do, we are focused on building a world-class ML engineering workflow, ensuring seamless training, deployment and monitoring of our predictive models.  As a core member of the Quant team, your work will be instrumental in developing the infrastructure enabling us to transform massive unstructured financial data sets into production-ready inference models that drive real-time trading decisions. You will work with technologies such as Python, Kubernetes, Ray and Airflow. In doing so, you will get a chance to develop and maintain the infrastructure needed for scalable, high-performance ML systems, ensuring a seamless pipeline from dataset generation through model prototyping to production deployment. Key responsibilities include: Designing, optimizing and maturing our infrastructure to support distributed model training and experiment tracking, as well as further developing internal Python tools for smooth interaction Building and automating robust data pipelines for dataset generation and feature engineering, while enabling data version control Developing software for rapid experimentation and deployment, including automated testing, continuous integration, and delivery Improve feature generation system- develop a feature store with definition and automated generation Transitioning to a more structured table based dataset store Implementing best practices in software development, ensuring maintainability, scalability, and efficiency of ML systems through automated testing and real-time monitoring, Improving model deployment workflows, focusing on minimizing latency, ensuring reproducibility, and enabling fast iterations You will get instant validation of your work and experience short feedback cycles, where going from inception to deployment can be a matter of hours. There will be no red tape to cut and no sales people to consult. About the team All of this will happen in an informal atmosphere where technical discussions are valued, and you are encouraged to take ownership of, and pride in, your work. You will impact the direction of the Quant team, prioritize your work and choose the tools that get the job done. We are a tight-knit modeling team with passionate quantitative researchers covering 9 nationalities – a mix of PhD’s and MSc’s with expertise in statistical analysis, mathematical modeling and machine learning. We enjoy collaboration and are always willing to lend a helping hand. We are working alongside two other teams of software developers and traders that implement and conceive the mathematical models together with us. What makes this constellation work great is that all of the individual team members write code and are willing to engage on a deep, technical level of understanding. About you A scientific and inquisitive mind Fluency with computer science fundamentals, specifically data structures and algorithms Experience working with ‘out-of-core’ datasets Experience with Python, in particular the data preprocessing, ETL pipelines and distributed systems Familiarity with strongly typed languages like C# or C/C++ or Rust A few years of experience in production-grade environments working with Machine Learning based applications  Experience with distributed computing frameworks (for example Spark, Ray or Dask) Familiarity with containerization technologies (f