Amel – Python, React, Typescript

A production-grade data annotation platform, developing full-stack and backend systems for high-demand environments with a focus on scalability, performance, and reliability. Worked within highly selective international remote engineering teams and collaborated in fast-paced, high-standard development workflows.

• Developed internal AI automation tools in Python to streamline agency delivery pipelines;
• Engineered Python-based middleware for LLM orchestration and automated data transformations;
• Built real-time authentication and data systems that reduced API latency by 30%;
• Optimized CI/CD and development workflows, cutting deployment time by 40-60%;
• Delivered 5+ production-grade apps, achieving Lighthouse performance scores of 90+.

Developed and delivered multiple production-grade web applications focused on high performance, scalability, and user experience, consistently achieving strong optimization metrics. The project also included building internal AI automation tools and Python-based middleware for LLM orchestration to streamline operational and agency workflows.

• Led backend modernization from legacy systems to a Python and Go architecture, improving application performance by 35-50%;
• Developed high-performance REST APIs using Python to support dashboards and CMS integrations for over 100 active users;
• Optimized state management and backend query logic, reducing unnecessary API requests and server load by 40%;
• Implemented automated data validation scripts in Python to ensure data integrity across the React-based frontend;
• Enhanced platform stability by streamlining data flow between the Python backend and real-time frontend services.

A conversational AI platform for healthcare service providers that automates patient booking and replaces traditional contact forms with intelligent chat-based interactions. The system significantly improved lead response times and reduced administrative workload through automated scheduling and communication workflows.

• Developed a conversational AI engine in Python that replaced traditional contact forms with automated patient booking;
• Integrated real-time scheduling and PMS workflows to provide fully automated booking for dental clinics;
• Improved lead response times by 85% and increased appointment conversion rates by 30-45%;
• Reduced front-desk administrative workload by over 60% across all deployed clinics;
• Engineered the system to handle complex natural language queries and patient data transformations.

Led a backend modernization initiative by transitioning core systems to a Python and Go-based architecture, improving overall system performance by up to 50% and enhancing scalability. Additionally, developed and maintained REST APIs supporting integrations for over 100 active users.

• Architected a serverless system using AWS Lambda and SST.dev;
• Integrated Weaviate vector database for semantic search and retrieval using Python;
• Designed secure access control and policy-based data permissions;
• Built APIs handling requests with optimized performance and scaling.

Architected and implemented a serverless backend system using AWS Lambda to ensure scalable, cost-efficient, and event-driven processing. Integrated Weaviate as a vector database to enable semantic search capabilities, improving retrieval accuracy and supporting AI-driven query handling. The system was designed with a focus on modularity, low operational overhead, and efficient handling of dynamic data workloads.

• Architected serverless system using AWS Lambda and SST.dev;
• Integrated Weaviate vector database for semantic search and retrieval using Python;
• Designed secure access control and policy-based data permissions;
• Built APIs handling requests with optimized performance and scaling.

Developed cloud-based monitoring and observability systems on Microsoft Azure to improve system reliability and operational visibility, resulting in a 50% reduction in incident response times. Additionally, automated data processing and pipeline workflows using Python to streamline operations and reduce manual intervention.

• Developed Azure-based monitoring and observability systems that reduced incident response time by ~50%;
• Built high-performance REST APIs in TypeScript, improving response times by 25-35%;
• Automated data pipelines using Python, saving 5+ hours per week of manual operations;
• Improved system observability and SLA compliance through custom alerting logic.

Designed and delivered custom backend systems and automation tools for international clients, focusing on process optimization and workflow efficiency. These solutions significantly improved operational performance by automating key business processes and reducing manual workload, resulting in efficiency gains of up to 70%.

• Built backend systems, APIs, and automation tools for international clients;
• Delivered integrations across multiple industries, improving operational efficiency by up to 70%;
• Provided technical mentorship and documentation for long-term client sustainability;
• Maintained consistent 5-star client satisfaction across long-term engagements.

Built and maintained over 100 UI systems supporting multiplayer game server environments, focusing on usability, performance, and real-time interaction. Developed Python-based tools to manage server infrastructure, automate asset handling, and efficiently process player data, improving operational workflows and system management.

• Built 100+ UI systems for multiplayer game servers using React and Vue to handle real-time data for thousands of users;
• Developed custom Python scripts to automate the deployment and synchronization of UI components across server environments;
• Designed reusable component systems that reduced development time by 30% and improved UI consistency;
• Optimized rendering performance and integrated frontend systems with backend logic using Python for data-handling tasks;
• Engineered Python-based tools to manage server assets and player data, streamlining overall operational efficiency.