Brandon Chase Harris

Owned technical decisions end-to-end for client-facing products, from requirements analysis and architecture through implementation, release, and post-production support. Built and evolved backend services and REST APIs with Java and Spring Boot, defining service boundaries, secure data access, observability, and maintainable domain logic. Delivered full-stack features using TypeScript, coordinating API contracts, validation, and error handling across frontend and backend. Improved reliability by driving automated test coverage (including integration testing), enhanced logging/exception handling, and added deployment checks to reduce production defects. Containerized services with Docker and automated CI/CD pipelines to improve deployment consistency. Resolved critical performance issues across application, database, and API layers by optimizing slow queries and costly paths while maintaining backward compatibility. Led engineering via code reviews, design discussions, pair programming

Architected Java and Spring Boot services powering complex web platforms, including RESTful APIs, business workflows, persistence layers, and integrations with external systems. Applied microservice and distributed-system patterns to separate responsibilities and enable independent testing and deployment. Built and reviewed data-access layers with JPA/Hibernate and SQL, resolving concurrency, transaction, and performance issues. Strengthened security by integrating authentication/authorization, validation, and secure API practices. Expanded automated test coverage using JUnit, Mockito, and integration tests with CI quality gates. Participated in architecture reviews, estimation, sprint planning, and incident analysis while mentoring peers and documenting reusable patterns. Standardized containerized development and deployment with Docker and CI/CD automation, and instituted application logging, metrics, and operational diagnostics.

Engineered end-to-end AI/ML solutions in Python, translating requirements into reproducible data pipelines, model-training workflows, inference services, and application integrations. Built machine-learning and deep-learning workflows for unstructured data, including preprocessing, feature engineering, evaluation, and production-grade inference. Delivered computer-vision components with Python and OpenCV, covering image preprocessing and detection/classification post-processing. Exposed model capabilities via versioned REST APIs and backend services with robust validation, error handling, and logging. Partnered with engineering teams to package and deploy model services using Docker and cloud infrastructure, balancing inference performance, reliability, and maintainability. Established rigorous testing/evaluation practices for AI features and standardized preprocessing to reduce train/serve inconsistencies. Defined measurable acceptance criteria with stakeholders, including data qualit