• Developed an algorithmic template based on optimization patterns in genetic algorithms and project requirements, accelerating the deployment of new algorithms and features by 80%.

• Improved building generation performance with asynchronous programming and front-end techniques, such as debouncing and lazy loading supporting interactive design updates, while reducing backend overload and keeping AI model response times under 0.1s for real-time user experiences.• Designed relational, structured JSON data models to standardize API interactions between front-end and back-end, enabling enhanced data retrieval and efficient search with a local NoSQL database.

• Conducted integration testing across multiple projects, identifying and resolving interactivity issues to ensure smooth collaboration between development teams.

• Integrated pre-trained generative AI models, including CycleGAN and StyleGAN, to generate constraint-based images, optimizing building generation in the AEC industry with AI-driven image generation. Enhanced user interactivity through a UI developed in collaboration with designers and project managers using PyTorch and TensorFlow.• Partnered with product managers and UX designers to enhance application performance and simplify the UI; led code reviews and pair programming sessions to maintain code quality and support knowledge sharing.

• Detected and mitigated malicious activities from bad actors by leveraging AWS Kinesis and Lambda, implementing high-throughput data filtering strategies at gigabytes-per-second based on customer identity.• Performed data mining on terabytes of real-time or historical data in a relational warehouse to uncover user behavior patterns, setting up logical and mathematical metrics to identify abnormal web activity and block malicious actors.• Conducted unit testing to validate the performance of scripts and queries, preventing resource overload in the AWS cloud environment and ensuring code accuracy while minimizing costs.