Tejas Lamba

Joined the deployment team at a Voice AI startup whose agents handle customer calls and WhatsApp in 20+ languages, owning client onboarding from discovery calls to go-live. Write and iterate on LLM prompts for voice-agent personas and call flows, including Hindi and Hinglish calls, debug live deployments from call logs and transcripts, and build Python scripts and API integrations connecting the platform to client CRMs and workflows.

Engineered low-power embedded AI firmware on STM32U5 microcontrollers with X-CUBE-AI, X-CUBE-MEMS1, and NanoEdge AI Studio, deploying on-device data loggers and anomaly detection models. Built a real-time computer vision system for Tic-Tac-Toe on an STM32MP257 microprocessor running OpenSTLinux, using YOLO11 for state classification, and programmed the game engine and serial communication drivers to coordinate moves with a physical robotic arm.

Built and optimized backend APIs in TypeScript for a high-performance fintech application. Redesigned the PostgreSQL schema for a core feature, cutting query times enough that the team noticed, and used Prisma ORM for type-safe queries with zero-downtime migrations while coordinating with PMs and frontend engineers on feature planning.

Provided IT support across 50+ corporate employees in the General & Administration department, resolving daily tickets covering hardware provisioning, system access control, and network connectivity, while maintaining Active Directory accounts and documenting recurring issues to reduce repeat tickets.

A clinical ML system with explainable risk assessment, built for the GDG on Campus Praxis 2.0 Hackathon. Combines an XGBoost classifier with SHAP explainability and a RAG pipeline over DeepSeek V3 for grounded, cited AI responses.

A full-stack web app that analyzes a user's photo and matches their expression/personality traits against 930+ Pokemon characters, using Google Cloud Vision and Gemini AI.

A React app that transforms text into a variety of Unicode styles and fonts with multi-language support and an instant live preview.

A lightweight, type-safe library that connects Discord.js bots to Lavalink audio servers with minimal setup and maximum flexibility. 187 stars on GitHub.

An in-game overlay for Minecraft Bedwars with multi-client compatibility, player stats, and game notifications, used by 200+ players.

An advanced Paper plugin for monitoring administrator actions on a Minecraft server, with Discord webhook integration and pattern-based command matching.

A modern dialog-based authentication interface for AuthMe on Paper servers, using Minecraft's native dialog system instead of chat prompts.

A lightweight Paper/Spigot plugin that reworks hostile mob behavior in boats, with per-mob configuration for escape chances, boat-breaking, and knockback.

A production-ready CLI for automated JavaScript dependency management, with intelligent update recommendations and semantic versioning safety checks across npm, yarn, pnpm, and bun.

An npm package that provides easy access to Musixmatch API endpoints for searching tracks, fetching lyrics, artists, and albums, with automatic authentication.

An advanced CNN-based system for recognizing handwritten digits with multi-digit sequence processing, automatic OpenCV segmentation, and a real-time GUI, achieving 98.5% accuracy on MNIST.

An end-to-end ML system for detecting fraudulent credit card transactions in real time, processing 284,000+ transactions at 99.9% accuracy with an interactive Streamlit dashboard.

• •Won 1st place with Team Syntax Terrors at an offline fintech hackathon at NSUT, out of 3,093 registrations.
• •Banks run scam detection and loan risk as separate departments, even though a scam often ends in a missed EMI. Two Strands scores both for the same customer and wires them together: a scam hit raises repayment risk, and debt stress puts the account on mule watch.
• •Live scam scoring with LightGBM and coercion rules, a mule-ring graph with GraphSAGE, and days-to-default prediction with a survival model. Every alert comes with SHAP reasons and a plain case note in English or Hindi.
• •Built on FastAPI, PostgreSQL and Redis Streams, with a React analyst console and a Kotlin Android payment app that reads call state during a payment.

• •Won 1st place with Team Deadbits out of 10,562 teams. Our track: build an AI assistant for investors doing M&A due diligence.
• •Built a RAG pipeline using Pathway AI and FAISS that answers questions from live data rooms (financials, contracts, cap tables) and updates in real-time as new documents arrive.
• •FastAPI backend handled document uploads and vector similarity search; the system cited sources and exported summaries for investor review.