I'm a recent (2026) AI/ML graduate, but most of my learning has come from building things that have to work outside a notebook. Having ~3 years of production experience and been into the programming/cs domain for ~7 years.
At MiyuLabs, my small hardware/software venture, I've built an ESP32-based interactive desk companion from firmware and device communication through the Go backend, database, deployment, and website. I've also built self-hosted email infrastructure with durable state, retries, rate limiting, and crash recovery. I enjoy owning the whole problem rather than handing off everything outside my immediate specialty.
On the ML side, I built a computer-vision quality-assessment pipeline using a custom YOLO segmentation model, reaching ~0.93 mAP50-95 on held-out data. That work included inference optimization and the surrounding application/API pipeline, not just model training. Other projects include EFNet, a compact CNN–ViT hybrid for few-shot open-set retraining-free face recognition for edge devices; MooNet, an offline-first lightweight cattle-breed classifier; and RLSwarm, a multi-agent reinforcement-learning environment with experiments comparing PPO and DQN across swarm intelligence behaviours.
I'm looking for ML/Applied AI, computer-vision/NLP, ML systems, or backend/distributed-systems engineering roles, especially on small teams where engineers are expected to investigate ambiguous problems, make pragmatic architecture decisions, and ship. I'm early-career by years, not claiming seniority I haven't earned, but I bring a broad, hands-on engineering background (breadth-wise) and a strong bias toward building and measuring real systems (depth-wise).
Technologies: Python, PyTorch, computer vision, deep learning, NLP Systems/LLMs, Agentic AI, ONNX Runtime, C/Go, TypeScript, SQL/PostgreSQL, FastAPI, Next.js, ESP32/FreeRTOS, Linux, Docker, CI/CD
Résumé/CV: https://rudraxd.in/resume.pdf
Email: [email protected]
Website: https://rudraxd.in ; GitHub: https://github.com/FireHead90544 ; LinkedIn: https://www.linkedin.com/in/rudraxd
I'm a recent (2026) AI/ML graduate, but most of my learning has come from building things that have to work outside a notebook. Having ~3 years of production experience and been into the programming/cs domain for ~7 years.
At MiyuLabs, my small hardware/software venture, I've built an ESP32-based interactive desk companion from firmware and device communication through the Go backend, database, deployment, and website. I've also built self-hosted email infrastructure with durable state, retries, rate limiting, and crash recovery. I enjoy owning the whole problem rather than handing off everything outside my immediate specialty.
On the ML side, I built a computer-vision quality-assessment pipeline using a custom YOLO segmentation model, reaching ~0.93 mAP50-95 on held-out data. That work included inference optimization and the surrounding application/API pipeline, not just model training. Other projects include EFNet, a compact CNN–ViT hybrid for few-shot open-set retraining-free face recognition for edge devices; MooNet, an offline-first lightweight cattle-breed classifier; and RLSwarm, a multi-agent reinforcement-learning environment with experiments comparing PPO and DQN across swarm intelligence behaviours.
I'm looking for ML/Applied AI, computer-vision/NLP, ML systems, or backend/distributed-systems engineering roles, especially on small teams where engineers are expected to investigate ambiguous problems, make pragmatic architecture decisions, and ship. I'm early-career by years, not claiming seniority I haven't earned, but I bring a broad, hands-on engineering background (breadth-wise) and a strong bias toward building and measuring real systems (depth-wise).