Yurii Borysenko

•Worked as part of a cross-functional team of 6–8 engineers building backend services for a healthcare SaaS platform used in clinical decision workflows •Developed backend services using Python (FastAPI) and Node.js to support data processing and real-time API requests across multiple systems •Gradually introduced AI-driven features, starting with semantic search and later integrating LLM-based workflows into production services •Built embedding-based retrieval components used to improve relevance of responses in symptom-checking features •Developed inference APIs for ML models, supporting real-time requests and improving response latency by around 30% •Reduced API response time by approximately 30% by optimizing database queries, indexing strategies, and introducing caching •Contributed to scaling backend services to handle roughly 2x increase in traffic without major architectural changes •Collaborated with product and data teams to refine system behavior and improve consistency of outputs across iterations •Mentored junior engineers and participated in code reviews, helping improve code quality and delivery consistency •Improved monitoring and logging practices using centralized metrics, helping reduce time spent diagnosing production issues

•Worked in a distributed team developing backend services for a healthcare SaaS platform supporting scheduling, billing, and patient workflows •Built REST APIs using Node.js and Express to handle high-frequency requests from frontend applications •Designed and maintained data pipelines for processing structured healthcare data across multiple services •Integrated third-party services such as payment and messaging systems used across client accounts •Reduced MongoDB query latency by approximately 25% in high-traffic endpoints through indexing and schema improvements •Implemented background job processing to support asynchronous tasks such as notifications, reporting, and data synchronization •Improved reliability of external integrations by introducing retry and timeout handling, reducing production failures •Collaborated closely with frontend and product teams to deliver features under evolving requirements and tight timelines •Gained initial exposure to ML-based features through integration of simple data-driven components into backend systems

•Worked within a team developing SaaS applications for international clients in a structured engineering environment •Built backend services using Java (Spring Boot) to support API-driven business workflows •Designed PostgreSQL schemas and queries, improving data consistency and query performance by around 20% •Implemented batch processing jobs for data handling tasks, reducing manual workload for recurring operations •Assisted in frontend development using React to improve usability of internal tools •Participated in testing and debugging processes, helping reduce regression issues across release cycles •Collaborated with senior engineers to understand system architecture and development best practices