Hi, I'm Amit Gohel - AI Engineer at Vivansh Infotech
I am fueled by the challenge of moving AI out of the lab and into the real world. I thrive where hardware meets software, building systems that ship and solve genuine problems for users who need real results.
At a glance
Curated signals on strengths, focus areas, and how they can help.
Earned top ten recognition among 70,000 participants in a competitive national Samsung TinyML hardware innovation challenge.
Architects robust, high-concurrency production LLM and RAG systems currently serving hundreds of active users at enterprise scale.
Provides expert guidance on full-stack AI deployment, optimizing everything from edge microcontroller performance to complex cloud-native architectures.
🚀 Career trajectory
Research Foundations
Explored deep learning and wearable computing at IIT Delhi and Gandhinagar.
✦ Academic Research
✦ Neural Networks
Enterprise Deployment
Scaled AI solutions for industrial EHS platforms at Vivansh Infotech.
✦ System Architecture
✦ Enterprise Scale
💪🏻 Superpowers
Full-Stack AI Architect
Connecting edge hardware to cloud-scale intelligence
Orchestrates multi-model LLM architectures for high-concurrency production environments
Integrates on-device TinyML inference with cloud-native retrieval pipelines
Optimizes end-to-end performance from 2KB microcontrollers to AWS clusters
Rapid Deployment Strategist
Transforming experimental prototypes into functional assets
Automated manual workflows to slash production times by orders of magnitude
Shipped six production-grade AI systems in under twenty-four months
Maintains 95% system reliability for high-volume enterprise user bases
Edge Intelligence Specialist
Bridging physical constraints with optimized machine learning
Implemented INT8 quantization for sub-2ms latency on constrained devices
Deployed award-winning hardware recognized in national Samsung innovation challenges
Engineered gesture recognition models with over 1,000 distinct configurations
I'm excited about
Exploring advanced collaborative opportunities to build next-generation autonomous AI agents
Connecting with founders building in the intersection of hardware and generative models
Contributing to high-growth technical communities to push the boundaries of production AI
I can help with
Architecting cost-efficient RAG pipelines for production-ready document retrieval systems
Guiding the transition from prototype code to scalable, containerized cloud infrastructure
Optimizing machine learning models for low-latency performance on edge computing hardware
I would love your help on
Insight into scaling enterprise AI platforms to handle exponentially growing concurrent user loads
Navigating strategic trade-offs between proprietary LLM integration and open-source fine-tuning
Connecting with mentors experienced in technical leadership and sustainable long-term engineering growth