Shashwat Manglam Jain

Created a competitive coding platform at CubexO as an internal tool for developer hiring assessments and team skill-building. Developers join real-time multiplayer contests, solve algorithmic challenges in a Monaco-based browser editor with syntax highlighting, auto-completion, and multi-language support (Python, Go, JavaScript, C++). Code is executed in isolated Docker containers with resource limits (CPU, memory, time) via a Go-based judge service that returns verdicts in under 2 seconds. Features ELO-based ranking, live leaderboards via WebSocket, and post-contest analytics showing time-per-problem and solution patterns. Handles 200+ concurrent contestants with horizontal scaling on Kubernetes.

Built a cross-platform mobile health application for a CubexO healthcare client to replace their paper-based patient management workflow. Features include appointment scheduling with push notification reminders, medication tracking with dosage alerts, real-time vitals monitoring via Bluetooth wearable integration (heart rate, SpO2, blood pressure), and encrypted medical record storage with role-based access for patients, doctors, and admins. The NestJS backend handles HIPAA-compliant data encryption at rest and in transit, with PostgreSQL for structured records and Redis for session caching. Served 800+ active patients across 3 clinics within the first quarter of launch.

Designed and developed a decentralized voting platform as an internal R&D project at CubexO to explore Web3 governance use cases. Voters authenticate via MetaMask wallet signatures and cast encrypted ballots using zero-knowledge proofs generated with Circom/snarkjs — ensuring vote privacy while maintaining verifiability. Smart contracts deployed on Ethereum Sepolia handle vote tallying, quorum checks, and result finalization with on-chain event logs. Ballot metadata is stored on IPFS via Pinata, and a React frontend displays real-time results with animated progress bars. Handled 5,000+ test votes during internal hackathon with zero double-vote incidents.

Built a real-time object detection API at CubexO for a logistics client's warehouse automation. The system uses a custom-trained YOLOv8 model fine-tuned on 50K+ labeled images to detect and classify packages on conveyor belts with 96.3% mAP. Exposed inference through a FastAPI service with Redis-backed request queuing, achieving sub-50ms latency per frame at 30fps throughput. Added a drag-and-drop React UI for uploading images, reviewing detections with bounding box overlays, and retraining the model with corrected labels. Deployed as a serverless function on AWS Lambda with model weights cached in S3 for cold-start optimization. Monitoring with Prometheus tracks model drift and accuracy degradation over time.