Aryan Anand

Ninestars Information Technologies, Bangalore

• Owned the delivery stack for multi-service applications using a 5-process Docker Compose topology spanning ASGI, Celery beat and queue-specialised workers; maintained Jenkins CI, a tmux development orchestrator, and idempotent setup automation for application dependencies.
• Automated infrastructure provisioning by health-checking and configuring PostgreSQL/TimescaleDB, Redis, RabbitMQ and S3-compatible storage, provisioning broker vhosts and quorum queues directly from application queue definitions to prevent topology drift.
• Engineered distributed database migration tooling with daemon-based processing, Unix socket IPC and background thread pools for parallel extraction and direct-to-S3 upload of 60M+ row databases, with live progress reporting, dynamic concurrency and graceful stop.
• Architected multi-tenant backend services using Django, PostgreSQL, Redis, RabbitMQ and Celery, including fail-closed tenant isolation across 33 tables, distributed task orchestration, idempotent processing, WebSocket progress streams and background job execution; shipped 170+ versioned builds in seven weeks backed by 70 scripted test suites.
• Built distributed ML processing across 10 RabbitMQ quorum queues with transactional fan-in and redelivery-safe processing, executing 10,700+ inference jobs across 2,500+ completed multi-stage runs; deployed services across ASGI and specialised worker processes with containerized service orchestration.

TIH IoT, IIT Bombay, Mumbai

• Built a U2-Net pipeline for automated image masking and applied CNNs on NDVI and hyperspectral imagery for vegetation segmentation (11% gain), preprocessing, augmentation, and hyperparameter tuning for deployment.

Python, PyTorch, TensorFlow, Llama, React.js, Node.js, MongoDB, Git

• Built a super-resolution GAN (TensorFlow) for MRI/CT clarity achieving 92% accuracy on 54,000 samples, and a CycleGAN (PyTorch) for bidirectional MRI-to-CT translation; designed a hybrid Vision Transformer-CNN for multi-label disease classification with 88.4% accuracy.

Python, OpenCV, TensorFlow, Google Cloud, Git

• Developed a real-time hand sign language-to-speech system using ResNet-18 and OpenCV achieving 97% accuracy and 200ms latency on low-end devices, with Google TTS API integration.