Vladislav Sayenko

· AI-Powered Predictive Lending Model (Golang, AWS Lambda, TensorFlow, PyTorch): Built a real-time AI-powered risk assessment engine using Golang, Python, TensorFlow, PyTorch, and AWS Lambda, enabling advanced credit scoring and fraud detection. Optimized model inference pipelines, reducing loan default rates by 25% and improving lender decision-making accuracy.

· Fraud Detection with AI & Node.js: Designed an AI-driven fraud detection system leveraging NestJS, Kafka, and Python, TensorFlow, improving fraud detection accuracy by 35%.

· GraphQL-Powered Financial API (Golang & Node.js & React.js): Developed GraphQL APIs for financial data aggregation, improving transaction processing efficiency by 40%.

· Serverless Loan Processing System (Node.js & AWS Step Functions): Migrated a monolithic financial system to serverless microservices, cutting response times by 50%.

· Data Streaming Optimization (Kafka & Node.js): Built event-driven Kafka pipelines, reducing message processing latency by 40%.

· Vue.js-Based Risk Analytics Dashboard (Node.js & Golang APIs): Created a Vue.js dashboard for financial risk analysis, reducing data processing delays by 35%.

· OAuth 2.0 & RBAC Security Implementation: Integrated AWS Cognito & OAuth 2.0 authentication, ensuring HIPAA & PCI DSS compliance.

· AI Model Deployment with AWS SageMaker & FastAPI: Automated AI model training pipelines, increasing fraud detection efficiency.

· Cloud-Native Infrastructure with Terraform: Deployed Terraform-based infrastructure, cutting provisioning times by 60%.

· Performance Tuning (Golang & PostgreSQL): Optimized SQL queries, reducing database response times by 45%.

· Event-Driven Notifications (Node.js & Redis Pub/Sub): Built a real-time notification service, improving engagement rates.

· AI-Powered Patient Monitoring (Golang & TensorFlow): Built an AI-driven health monitoring system that analyzed patient vitals in real-time, increasing adherence to treatments by 30%.

· EHR-Integrated Telemedicine Platform (Node.js & Angular): Led the modernization of a HIPAA-compliant patient portal, enhancing usability, security, and real-time video consultation support.

· Vue.js & React-Based Analytics Dashboards: Developed real-time patient analytics dashboards with D3.js, WebSockets, and Redux, enabling doctors to monitor live health data efficiently.

· GraphQL API for Medical Supply Chains: Designed and deployed GraphQL APIs in Node.js & Golang, facilitating real-time pharmaceutical inventory tracking and distribution optimization.

· Automated AI Training Pipelines (AWS Lambda & Golang): Engineered an automated AI model training and deployment pipeline, reducing model deployment time by 50%.

· DynamoDB & Node.js for Compliance Tracking: Developed an event-driven compliance monitoring system that ensured adherence to regulatory policies by leveraging AWS Lambda, SNS, and SQS.

· Vue.js-Based Drug Research Dashboards: Created an interactive clinical trial dashboard using Vue.js, D3.js, and GraphQL, streamlining data visualization for pharmaceutical research.

· AI-Powered Diagnosis System (Golang & TensorFlow.js): Developed an AI-driven diagnostic assistant that analyzed patient symptoms and recommended potential treatments, improving diagnostic accuracy.

· Azure Kubernetes-Based Secure Data Pipelines: Designed Kubernetes-powered secure data pipelines that complied with HIPAA regulations, ensuring scalable & encrypted patient data transfer.

· AWS Step Functions for Automated Claims Processing: Developed automated insurance claims workflows using AWS Step Functions, SQS, and DynamoDB, reducing processing times by 35%.

· React-Based Patient Management System: Developed a React.js-powered medical management platform that streamlined patient record management and improved data retrieval speed by 40%.

· HIPAA-Compliant Authentication System: Integrated OAuth 2.0, JWT, and AWS Cognito, ensuring secure and role-based access to sensitive medical records.

· GraphQL API for Drug Research: Built a GraphQL API to streamline clinical trial data processing, reducing data retrieval latency by 35%.

· Angular-Based Drug Inventory System: Designed a real-time inventory tracking dashboard with RxJS and WebSockets, reducing stock discrepancies by 30%.

· Node.js & AI Diagnosis System: Created an AI-powered decision-making tool for automated disease diagnosis using Golang and TensorFlow, improving diagnosis accuracy by 28%.

· Vue.js-Based Compliance Monitoring Tool: Built a Vue.js dashboard for regulatory compliance audits, streamlining internal processes and ensuring real-time monitoring of audit trails.

· AWS Lambda for Serverless Data Processing: Developed serverless ETL pipelines using AWS Lambda, Step Functions, and DynamoDB, reducing processing times by 50%.

· Automated AI-Powered Drug Side Effect Prediction (Node.js & TensorFlow): Implemented machine learning models to predict adverse medication reactions, reducing incorrect prescriptions by 20%.

· Event-Driven Clinical Data Processing: Integrated Kafka and Golang microservices to enable real-time analytics and processing of patient data, enhancing reporting accuracy.

· Secure Medical Record System with AWS DynamoDB: Designed and optimized secure, cloud-based data storage for electronic medical records, ensuring 99.9% availability.

· Node.js API for Real-Time Appointment Scheduling: Developed a high-performance scheduling system using Node.js, Redis, and PostgreSQL, reducing appointment booking failures by 40%.