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Aditya Singh
From India 09:12 AM (GMT+04:00)
$10/hr or $1,499/yr

Active 1 hour ago


Member since Oct 2026

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Full Stack AI Engineer

Artificial Intelligence Engineer
Available for hire
Years of experience
2+ years
Experience level
Mid-level
Available for
Full-time, Part-time, Contract, Freelance
Available from
26 Oct 2026

I’m a Full-Stack AI/ML Engineer who enjoys turning complex technical challenges into scalable, real-world solutions. At Altarium Consulting, I built real-time computer vision pipelines processing 8+ concurrent camera streams at ~25 FPS with 0.87 [email protected], reduced inference latency by 35%, peak memory usage by 60%, and stream reconnection downtime by 80%. I also developed backend services supporting 5,000 daily active users.

My experience spans Python, Node.js, PyTorch, OpenCV, TensorRT, and production-ready AI systems. I’ve also built a surgical instrument segmentation pipeline achieving 0.89 mean IoU at 45 FPS, and a product defect detection system delivering 97.3% defect recall.

What makes me unique is my ability to bridge AI/ML and full-stack engineering, taking solutions from model development to reliable production deployment. I’m looking for a role where I can solve challenging problems, build impactful AI-powered products, and grow alongside a strong engineering team.

Employment History

Full Stack AI/ML Engineer at Altarium Consulting Pvt. Ltd. 2025 - 2026
• Architected an asynchronous RTSP ingestion layer supporting 8+ concurrent camera streams on a single-threaded Node.js worker, using non-blocking frame queuing to isolate stream failures and eliminate cross-feed interference. • Built a real-time computer-vision pipeline running YOLO-based detection for crowd counting, occupancy monitoring, and entry-gate analytics, achieving 0.87 [email protected] at ~25 fps on a single NVIDIA T4. • Engineered end-to-end pipeline: RTSP ingest → OpenCV preprocessing → YOLO inference → NMS/tracking → structured alerts and occupancy dashboards. • Reduced stream reconnection downtime by 80% via exponential-backoff reconnection with per-stream health tracking, maintaining detection stability across 2-3 hour live events. • Optimised frame lifecycle management - ingest through post-inference cleanup - cutting peak memory usage ~60% and sustaining throughput without degradation during full events. • Delivered structured detection events to downstream consumers within 3 seconds of occurrence via an event-driven pub/sub layer over WebSockets. • Optimised inference latency by ~35% and surfaced live detection metrics through Grafana dashboards. • Built and maintained backend services for the ticketing platform using Node.js, Prisma ORM, and PostgreSQL, serving 5,000 daily active users.
Full Stack Developer Intern at CHL Softech Pvt. Ltd. 2024 - 2024
• Containerized TensorFlow-based machine learning models using Docker, reducing model initialization latency by 40% and ensuring reliable real-time inference in production environments. • Designed a Flask backend with JWT authentication and role-based access control, optimizing CRUD operations to support 500+ concurrent users with stable session management. • Developed robust RESTful APIs and optimized relational database schemas to streamline microservice data flow, lowering integration complexity.

Education

Bachelor of Technology in Computer Science Engineering at Guru Gobind Singh Indraprastha University 2021 - 2025