Bhavesh Bhansali

Backend API Development & Optimization: Developed and maintained critical backend services for SDPDS and Offpeak Hour features using Java and Python, focusing on scalability and robustness. Created all external APIs for Offpeak Hour scheduling using Java Guice Coral framework.

Enhanced tooling and automation for scheduling, rescheduling, and aborting software updates across distributed domains.

Large Component Upload System Design: Architected and implemented a solution to enable large component uploads (up to 5TB) in SDPDS by optimizing multipart copy operations and managing S3 bucket policies, overcoming 5GB size limitations. Delivered end-to-end code and testing, significantly improving system capacity and reliability.

CI/CD Pipeline Enablement & Automation: Led MW2 allowlisting for 6 SDPDS pipelines, enabling full CI/CD automation including MVP regions. Automated metric and alarm replication across multi-account AWS environments, reducing manual intervention and improving monitoring fidelity.

Concurrency & Safety Enhancements: Designed and implemented idempotent domain-level and node-level safety actions, reducing operational noise and improving system stability. Developed concurrency control mechanisms to handle distributed update workflows safely.

System Decoupling & Containerization Support: Decoupled SDPDAgent from OS Apollo environment path to support containerized deployments, enabling seamless configuration and initialization in container environments.

Integration Testing & Pipeline Reliability: Revamped integration test strategy for SDPDS pipelines, reducing timeout issues and enabling complete CI/CD pipeline automation. Implemented automated integration tests for Offpeak Hour features, ensuring high code quality and deployment confidence.

Security & Encryption: Designed and implemented Customer Master Key (CMK) integration for Nexus, enabling secure encryption and decryption workflows across multiple AWS services including S3 and DynamoDB.

Mentorship & Leadership: Guided interns on key projects such as rescheduling software updates post self-service hours, providing design reviews and code guidance to ensure successful delivery.

Key Achievements: Reduced manual operational overhead by automating pipeline promotions and monitoring.

Enhanced large-scale data upload capabilities, supporting up to 5TB component sizes.

Improved system reliability through idempotency and concurrency control designs.

Delivered multiple critical bug fixes and root cause analyses under tight deadlines.

Built and deployed ML-powered movie recommendation engine using AWS SageMaker and collaborative filtering models.

Automated network migration from Cisco to Aruba using custom Python tools, reducing transition effort by 80%.

Integrated real-time automation pipelines using AWS Lambda, SNS, and DynamoDB for dynamic configuration systems.