Hi, I'm Shubham
I'm a Software Engineer based in Washington, DC, passionate about creating intelligent, user-centered systems using Machine Learning and modern software tools.
Currently seeking full time opportunities
About Me
Hi, my name is Shubham, and I'm a passionate Software Engineer and problem solver currently pursuing my studies at George Washington University. I have a strong foundation in Full-Stack Development, Android Development, and AI Development. I'm actively seeking opportunities where I can contribute to building secure, scalable, and user-focused tech solutions.
I specialize in crafting web and mobile applications using technologies like JavaScript, TypeScript, React, Node.js, Kotlin, Python, Firebase, and AWS cloud services. I enjoy developing intuitive user experiences, writing clean code, and building infrastructure that scales.
I thrive in collaborative and fast-paced environments, whether I'm developing secure APIs, enhancing backend performance, building Android applications, or exploring intelligent systems through AI development. I'm also diving deeper into Machine Learning to create data-driven, impactful solutions that can learn, adapt, and solve real-world problems.
Let's connect and build something amazing together!
Experience
Software Engineer @ Zydus
Aug 2019 - Nov 2023
Python, Node.js, AWS Lambda, API Gateway, DynamoDB, CloudFormation, Prometheus, Grafana, CloudWatch, Jenkins, WebSocket
- ▹Contributed to the development of NeuroScan, a Python-based AI diagnostic system designed to analyze EEG signal data in real-time for early detection of neurological anomalies. Collaborated on model tuning using TensorFlow and deployed inference APIs with FastAPI, resulting in 92% accuracy on benchmark datasets and enabling real-time clinical alerts.
- ▹Led a 6-member team to build Project Sentinel, a real-time healthcare incident response dashboard using AWS Lambda, DynamoDB, and WebSocket APIs. Reduced incident resolution time by 35% and improved visibility across regional SRE teams.
- ▹Architected and deployed OpsStream, a CI/CD observability suite tailored for healthcare pipelines. Integrated Prometheus, CloudWatch, and Grafana to deliver real-time deployment health insights, reducing detection time for post-deployment issues by 50%.
- ▹Built and maintained microservices with Node.js and Express.js, leveraging AWS Step Functions and CloudFormation for zero-downtime multi-region deployments cut provisioning time by 40%.
- ▹Automated regression testing and monitoring with Jenkins and CloudWatch, resolving the top three recurring crash patterns and stabilizing production uptime.
- ▹Mentored junior engineers and collaborated with product and QA teams to ensure secure and reliable deployments across all environments.
Projects
I'm building a project called VoiceSense, a real-time emotion detection system that analyzes voice input to classify emotional states like happiness, sadness, or anger using deep learning models. I used Python, PyTorch for model training, Librosa for audio feature extraction, and Coqui TTS to generate voice responses that match the detected emotion. The system also uses FastAPI for backend inference and a React-based frontend where the tile color changes dynamically based on the emotion detected. I'm currently developing a new feature to bring this into live video calls, where the system will process voice in real time and update the participant's tile color during the call to reflect their emotional state.
A full-stack AI chatbot platform built from the ground up with conversational memory and intelligent code analysis. Features include follow-up question support using LangChain, automatic routing between chat and code improvement pipelines, and real-time subscription management with Stripe integration. Built with Next.js, TypeScript, and Tailwind CSS frontend, Node.js/Express backend, PostgreSQL with Prisma ORM, and Clerk authentication. The AI engine runs locally using Ollama with LLaMA 3, eliminating dependency on external APIs. Includes tiered subscription plans (Basic, Pro, Plus) with usage limits and instant UI updates. Demonstrates advanced AI integration with production-ready features like JWT security, real-time data tracking, and seamless user experience.
This project is an AI-powered Pitch Deck Generator that allows users to input their startup idea, industry, and preferred tone to automatically generate a professional pitch deck. It uses LangChain with Ollama (LLaMA 3) on the backend to create slide content, Puppeteer to generate a downloadable PDF with styled HTML templates, and a modern Next.js + Tailwind CSS frontend for a sleek user interface. Users can view the generated slides and download them as a well-formatted PDF presentation — making it a powerful tool for startup founders and entrepreneurs to quickly craft compelling investor decks.
What's Next
Get In Touch
I'm currently looking for Full Time jobs.
Whether you have a project to discuss or just want to say hi, my inbox is open for all!
Designed and built by Shubham Avhad