About Me
Malay Jain is an AI & ML engineer in the making who enjoys turning complex ideas into practical, real-world solutions. From building voicebots with telephony integration to deploying RAG-based chatbots on cloud platforms, he focuses on creating systems that are fast, intelligent, and genuinely useful. He has hands-on experience with Python, FastAPI, SQL, and machine learning, and has worked on projects ranging from cardiovascular disease prediction to energy forecasting models. A curious builder at heart, Malay has earned recognition in national hackathons, research events, and large-scale data challenges. He has presented research at IIT Mandi, secured a copyright for his ML model, and actively contributes to the tech community as a GDG Campus Ambassador and mentor to juniors. When he’s not coding or experimenting with AI agents, you’ll find him leading tech events, exploring new tools, or figuring out how to make machines just a little bit smarter every day.
Skills & Technologies
Work Experience
Software Developer Intern
Developing AI agents and backend automation tools using Python, FastAPI, and SQL. Building and optimizing APIs, with deployment support through Docker. Collaborating in a team to deliver scalable AI-driven solutions.
AI Intern
Contributed to AI and R&D initiatives, focusing on real-world problem-solving in 2 projects. Assisted in data preprocessing, model experimentation, and performance evaluation.
Projects
Voice Bot with Telephony Integration using LiveKit Platform
Built an AI Voice Bot with LiveKit SIP for inbound/outbound calls and automated CRM integration. Developed dynamic room routing and persona-based replies using dialed-number metadata. Optimized cut call latency by 30% through Redis caching and modular FastAPI + LiveKit design.
GCCD Bhopal AI Chatbot
Built a Flask-based chatbot using Gemini 1.5 Flash + LangChain RAG pipeline for 100+ real-time queries. Hosted on both GCP and Azure with custom SSL domain. Reduced manual query handling by 80%, supported Hinglish, auto-logged chats to Google Sheets.
A Model for Prediction of Cardiovascular Diseases Using ML
Built a Random Forest model trained on 1,000+ samples with optimized feature selection. Achieved 81% accuracy and 95% precision on unseen test data. Code copyrighted; research paper under review at international conference.
Education
B.Tech.
General
12th PCM
General
10th
General