Vansh Bhatnagar
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Building AI systems and scalable backends with expertise in PyTorch, TensorFlow, and cloud technologies. Specializing in neural networks, retrieval-augmented generation, and generative AI.
About Me
Full-stack Developer & Machine Learning Engineer
I'm a full-stack developer and machine learning engineer who loves building AI systems and scalable backends. I've spent much time working with PyTorch and TensorFlow to build and fine-tune ML models.
My bread and butter are neural networks, retrieval-augmented generation, and generative AI. I specialize in designing end-to-end machine learning pipelines and crafting high-performance distributed systems in the cloud.
Location
Udaipur, India
vanshbhatnagar445@gmail.com
Phone
+91 9785366298
Education
B.Tech in Computer Science
Technical Skills
My technical expertise spans across various domains, from AI/ML development to cloud infrastructure and backend systems.
AI/ML Development
Cloud & Infrastructure
DevOps & Automation
Backend Development
Database Management
Testing & Quality Assurance
Professional Experience
My journey through various tech roles and projects
AI/ML Intern
ShadowFox Technologies
Machine learning algorithms were used to improve application performance, resulting in 25% reduction processing time and 10% improvement in accuracy.
The integration of the OpenAI API in the platform played a key role in increasing user engagement by 35% and satisfaction by 70% for the chatbot system.
Full Stack Intern
CodeAlpha
I designed user-friendly mobile applications making it simple to submit forms by combining data and geo-tag functionality, offering 40% faster submissions and 15% less errors. I also utilized such tools as Nginx for smooth server maintenance and Gradle for significantly simplifying build process.
To accelerate and enhance deployments I embraced Docker container. Not only did this approach reduced deployment times by 50% but it also enhanced the overall performance of the system as a whole, enhancing the web applications' solidity and scalability.
Cloud Computing Intern
Acmegrade
Conducted cloud computing trend analysis for AWS, Azure, and GCP to identify the most appropriate market opportunities.
Applied Docker containerization technology in the existing cloud configuration which improved the resource utilisation time by 40%.
Collaborated with cross-functional teams in debugging and resolving technical issues with respect to cloud computing platforms, achieving a 20% boost in system uptime.
Featured Projects
A showcase of my recent work in AI/ML and full-stack development
News Webpage Semantic Analysis Tool
Built an NLP-based web app for analysis of news articles utilising Python, spaCy, and TextBlob which extracts text and generates output such as entities, sentiment, and keywords. Integrated Groq's AI API to generate refined article summaries and built an intuitive Gradio interface for seamless user interaction.
AI Based Grass and Milk Production Predictor
Improved a ML-based computer vision system to scan farm photos to evaluate the quality of the grass and forecast yield. The solution uses image processing algorithms to scan important features such as colour, texture, and morphology to produce quality indexes and weight prediction with high accuracy.
AI Based Disease Detector
Built an AI-based diagnostic system based on deep learning models that identify respiratory illness (lung cancer, tuberculosis, pneumonia) from chest X-rays. Integrated convolutional neural networks to process medical images with high accuracy across a large patient population while following strict validation procedures and reducing false negatives.
Customer Feedback Chatbot
Enhanced an AI-powered customer feedback analysis system with NLP and AI technologies to analyse customer interactions via a sophisticated chatbot. The system minimizes the need for manual sentiment analysis and provides actionable insights, which can be used for data-driven decision-making in customer experience optimization. This optimisation helped increase the productivity by 36%.
MCHN Monitoring App
A React Native-powered child vaccination tracking system was rolled out to monitor unvaccinated children across Udaipur. This cutting-edge system elegantly integrates Google Maps for geo-tagging, enabling accurate location tracking and easy visualization of vaccination coverage. Leveraging cutting-edge mobile development practices and hosting the app on a Linux server instance, the system improved data accuracy by 45% and increased the effectiveness of vaccination campaigns by a whopping 60%. This solution minimizes manual record-keeping considerably, improves tracking, and provides healthcare workers with actionable insights that can drive vaccination rates higher, leading to better public health outcomes.
LangGraph CyberSecurity Agent
LangGraph CyberSecurity Agent was developed and designed as a robust multi-agent cybersecurity tool on the basis of the LangGraph framework to deal with stateful, large-scale applications using LLMs. The agent offers high-level protection by executing detailed vulnerability scans using tools such as Nmap, Gobuster, ffuf, and SQLMap, completing all the scans within 2 minutes. It supports real-time system monitoring, complete reporting, and remediation, all aimed at securing infrastructures against possible attacks. With an interactive Streamlit-based GUI, it offers simple configuration and policy management. It can be customized to the core using environment variables and deployable across multiple platforms, thus an efficient and flexible solution to cybersecurity problems of today.
Achievements
Recognition and awards for my work in technology and innovation
Letter of Recognition from WHO
Developed an interactive monitoring platform with geo-tagging for un-vaccinated children.
Created a comprehensive monitoring system to track day-to-day vaccination drives.
Runner Up in Code Red 4.0 Hackathon
Led a team in creating an AI-driven medical diagnostic system for rural societies.
Developed and implemented an artificial intelligence model for the analysis of X-rays and CT scans to predict potential diseases.
Contributed towards making advanced medical diagnostics accessible to deprived rural societies.
Winners SIH 2023 (Internal Round)
Developed an interactive dashboard for monitoring and plotting air and water quality parameters efficiently.
Created a comprehensive monitoring system to track environmental quality indices in near-real-time.
Get In Touch
Have a project in mind or want to discuss a potential collaboration? Feel free to reach out!