Machine Learning Infrastructure Engineer, Fintech

Optasia is a fully enabled B2B2X financial technology platform covering scoring, financial decisioning, disbursement and collection. We are committed to enabling financial inclusion for all. We are changing the world our way . We are seeking for enthusiastic professionals, with energy, who are results driven and have can-do attitude, who want to be part of a team of like minded individuals who are delivering solutions in an innovative and exciting environment. Data is at the core of Optasia growth plan and the ML Engineering team is a significant contributor to Optasia’s success and growth, achieved through data driven insights and decision making. We are currently leveraging and ingesting data from multiple sources into our large-scale big data clusters and develop and run multiple analytical pipelines, over a state-of-the art big data technology stack. ML Infrastructure Engineers are significant contributors of the company’s data driven automated decision making and risk management. They have extensive experience with machine learning flows, and the development and deployment of advanced algorithms. Their responsibilities include (i) building robust ML pipelines , (ii) designing and developing statistical and machine learning algorithms, and (iii) operationalizing these solutions to strengthen in credit risk management — directly contributing to Optasia’s success. What you will do Contribute to the design and development of the microservices and tools that support Machine Learning lifecycle at Optasia. Contribute to the design and delivery of scalable, real-time microservices used globally. Drive continuous improvements in the development lifecycle in collaboration with the team. Design, develop and maintain large-scale Spark jobs using PySpark and Scala. Build and manage CI/CD pipelines with Jenkins. Develop automation scripts using Python or Bash. Develop and deploy scalable Airflow pipelines that support the Machine Learning lifecycle. Perform data exploration and analysis to scope, build, and iterate on Machine Learning proof-of-concepts (PoCs). Partner with Engineers and Credit Risk team to design and deliver solutions that drive business value at Optasia. Optimize the codebase through Spark job tuning and refactoring. Drive improvements to our feature engineering engine to support more efficient ML workflows. What you bring Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, or Informatics. 2+ years of industry experience with an engineering background. Solid understanding of core Machine Learning concepts and MLOps. Good knowledge in Python and PySpark (or Scala). Strong knowledge of the Hadoop ecosystem. Proficiency in SQL. Proficiency in Linux. Strong knowledge of end-to-end API development and deployment. Proficiency in building and managing Dockerized applications. Experience with workflow orchestration tools such as Airflow (or similar). Familiarity with CI/CD best practices. Ability to meet tight deadlines, work under pressure, and maintain strict attention to detail. Awareness of emerging technologies, with the ability to quickly learn and adapt to new tools and frameworks. What we offer: 👟 Flexible remote working 💸 Competitive remuneration package 🏝 Extra day off on your birthday 💰 Performance-based bonus scheme 👩🏽‍⚕️ Comprehensive private healthcare insurance 📲 💻 All the tech gear you need to work smart Optasia’s Perks: 🎌 Be a part of a multicultural working environment 🎯 Meet a very unique and promising business and industry 🌌 🌠 Gain insights for tomorrow market’s foreground 🎓 A solid career path within our working family is ready for you 📚 Continuous training and access to online training platforms Optasia’s Values 🌟 #1 Drive to Thrive: Fully dedicated to evolving. We welcome all challenges and learning opportunities. #2 Customer-First Mindset: We go above and beyond to meet our partners’ and clients’ expectations. #3 Bridge the Gap: Knowledge is shared, information is exchanged and every opinion counts. #4 Go-Getter Spirit: We are results oriented. We identify any shortcomings that hold us back and step up to do what’s needed. #5 Together we will do it: We are committed to supporting one another and to understanding and respecting different perspectives, as we aim to reach our common goals.