Dheeraj Upadhyay 🚀

Full Stack Engineer

Noida, Uttar Pradesh, India1 yr 7 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Developed an AI chatbot improving customer response efficiency by 40%.
Built scalable real-time chat systems for enhanced user experience.
Expert in MERN stack with a focus on user-centric applications.

Stackforce AI infers this person is a Full Stack Developer specializing in SaaS applications with a focus on user experience and scalability.

Contact

dheerajupadhyaycodes@gmail.com LinkedIn

Skills

Core Skills

Full Stack DevelopmentApi Development

Other Skills

AWSAlgorithmsAmazon Web Services (AWS)Artificial Intelligence (AI)Asynchronous workC (Programming Language)C++CRM DatabasesCascading Style Sheets (CSS)ClosureCloud Management PlatformCommunicationComputer NetworkContext apiData Structures

Experience

1 yr 7 mos

Total Experience

1 yr 7 mos

Average Tenure

1 yr 7 mos

Current Experience

Metageeks technologies

Full Stack Developer

Nov 2024 – Present · 1 yr 7 mos · Noida, Uttar Pradesh, India · Remote

Developed and deployed an AI-powered chatbot application to handle customer queries, improving response efficiency by 40%.
Built a Customer Relationship Management (CRM) tool to manage customer onboarding, billing, complaints, and service requests.
Built a real-time chat system using MERN Stack, Python, WebSockets, and Redis to enhance scalability and reduce message latency.
Designed APIs in Node.js/Express for customer data, service plans, and billing.
Built the frontend in Next.js/React for agents to track tickets, customer status, and payments.
Integrated MongoDB/PostgreSQL for storing customer data.
Added authentication & authorization (JWT/Clerk).
Collaborated with cross-functional teams to integrate telecommunication APIs (SMS/VoIP) into the chat platform, enabling seamless multi-channel communication.
Implemented microservices architecture with Node.js and RabbitMQ, improving fault tolerance and system performance.
Deployed applications on AWS (EC2, S3, Lambda, RDS) with automated CI/CD pipelines, ensuring 70.9% uptime.
Designed scalable NoSQL database schemas (MongoDB) for chat history and user management, supporting 10K+ concurrent users.
Enhanced customer experience by integrating NLP-based intent recognition, reducing human agent load by 30%.
Conducted code reviews, unit testing (Jest), and performance optimizations, resulting in 25% faster API response time.