Petri Juhani

Led full-stack development of a scalable fintech web application using Python (Django) and React.js, serving 5,000+ active users. Designed and integrated RESTful and GraphQL APIs to optimize data handling, reducing average response time by 40% and supporting the application's long-term vision. Managed end-to-end CI/CD pipelines and deployments on AWS using Docker, achieving 99.5% deployment success and enabling rapid iteration for client projects. Integrated ML-driven features including a real-time recommendation system, deploying models as APIs that processed 10,000+ daily requests for real-time inference. Collaborated with clients and data teams to monitor model performance, improving prediction accuracy by 15% and ensuring alignment with project goals in healthcare and fintech.

Developed full-stack applications using Python, React.js, and MongoDB, contributing to the system design of a data-intensive platform handling 1M+ documents. Optimized backend performance and database queries, improving API throughput by 60% and supporting efficient real-time data updates for end-users. Built and deployed cloud-native applications on AWS, utilizing Docker containers to enhance scalability and streamline the client hiring and onboarding process for new features. Engineered data preprocessing pipelines for ML workflows, including feature engineering, which accelerated model experimentation cycles by 30%.

Designed and maintained scalable REST APIs using Python, Node.js, and Java to support enterprise-level data services and integrations. Managed both relational and NoSQL databases, ensuring high availability and performance for reporting and analytics pipelines. Built data processing workflows that transformed TB-scale datasets, enabling data-driven decision systems for internal stakeholders.