AI / ML Engineering & Intelligent Systems

Turning Ideas
into Intelligent
Systems

Hello, I'm Vaibhav Gupta — a B.Tech CSE student and AI/ML engineer focused on building practical machine learning systems, intelligent applications, and scalable software products.

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vaibhav-portfolio
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vaibhav-portfolio
Hero.tsx
srccomponentssectionsHero.tsx
1import { useEffect, useState } from "react";
2import { ArrowRight } from "lucide-react";
3import { site, typingPhrases } from "@/data/portfolio";
4import { MacIDE } from "@/components/ide/mac-ide";
5
6function useTyping(phrases: readonly string[], speed = 55, hold = 1400) {
7 const [i, setI] = useState(0);
8 const [text, setText] = useState("");
9 const [del, setDel] = useState(false);
10
11 useEffect(() => {
12 const cur = phrases[i % phrases.length];
13 if (!del && text === cur) {
14 const t = setTimeout(() => setDel(true), hold);
15 return () => clearTimeout(t);
16 }
17 const t = setTimeout(
18 () => setText(del ? cur.slice(0, text.length - 1) : cur.slice(0, text.length + 1)),
19 del ? speed / 2 : speed,
20 );
21 return () => clearTimeout(t);
22 }, [text, del, i, phrases, speed, hold]);
23
24 return text;
25}
26
27export function Hero() {
28 const typed = useTyping(typingPhrases);
29
30 return (
31 <section id="home" className="relative overflow-hidden pt-32 md:pt-40 lg:pt-44">
32 <div className="container-x grid gap-14 lg:grid-cols-[1.05fr_1fr]">
33 <div className="reveal">
34 <div className="eyebrow">AI / ML Engineering & Intelligent Systems</div>
35 <h1 className="display-hero mt-6">Turning Ideas into Intelligent Systems</h1>
36 <p className="mt-8 max-w-xl text-[17px] leading-[1.7]">
37 Hello, I'm {site.name} — a B.Tech CSE student and AI/ML engineer.
38 </p>
39 <span className="font-mono text-[13px]">{typed}</span>
40 <a href="#projects" className="btn-primary">
41 View Projects <ArrowRight className="h-4 w-4" />
42 </a>
43 </div>
44
45 <div className="relative reveal lg:-mr-16 xl:-mr-28">
46 <MacIDE />
47 </div>
48 </div>
49 </section>
50 );
51}
52
Last login: today on ttys002
vaibhav@macbook vaibhav-portfolio %
0 2
Ln 1, Col 1TypeScript React
Portfolio
Favorites
Tags
iCloud synced
NameDate ModifiedSize
srcYesterday3 items
og-cover.png2 days ago412 KB
package.jsonToday1 KB
README.mdYesterday3 KB
4 items · 128.4 GB available
zsh — 100×30
Last login: 01:06:58 on ttys001
vaibhav@macbook~/dev/vaibhav-portfolio%
All Processes
Process Name% CPU▼MemoryThreadsPIDUser
Code Helper (Renderer)24.1812 MB388123vaibhav
node22.5501 MB228241vaibhav
Google Chrome11.91319 MB467710vaibhav
vite9.9293 MB148302vaibhav
Portfolio Dev Server8.4218 MB118402vaibhav
tsserver7650 MB188318vaibhav
WindowServer6.4315 MB12180root
Language Server (Tailwind)4.8181 MB98433vaibhav
npm3.596 MB58688vaibhav
Terminal2.169 MB68601vaibhav
Finder1.3144 MB8512vaibhav
git0.142 MB38712vaibhav
System load
102.0%
Idle 1.0%
CPU pressure
20%
12 processes

Engineering intelligence into
real-world products.

Machine learning, generative AI, problem solving, and scalable software engineering — brought together to build systems that create measurable impact.

PYTHONPYTORCHSCIKIT-LEARNNEXT.JSSUPABASEPOSTGRESQL
About

How Vaibhav Turns
Ideas Into Intelligent Systems

A combination of computer science fundamentals, machine learning, AI engineering, and product development.

The mind behind
the models

I'm Vaibhav Gupta, a B.Tech Computer Science & Engineering student at Raj Kumar Goel Institute of Technology (RKGIT), graduating in 2028.

My work focuses on machine learning, deep learning, generative AI, data-driven applications, and scalable software engineering.

Alongside AI development, I consistently practice data structures and algorithms to strengthen problem-solving fundamentals and write efficient software.

I enjoy turning complex technical ideas into practical products that solve real-world problems.

Explore My Journey
Education
B.Tech Computer Science & Engineering
RKGIT · 2024–2028
Engineering Focus
Machine LearningDeep LearningGenerative AILLMsNLPDSA
Current Direction

Building production-oriented AI applications and scalable software products.

How I Build Intelligent
Software Products

Four core capabilities that connect AI engineering, problem solving, and product development.

Machine Learning Engineering

Build data-driven systems using statistical learning, feature engineering, model evaluation, and explainable AI.

Signal
45K+ Records Modeled

Deep Learning & GenAI

Exploring neural networks, transformers, NLP, LLM applications, RAG systems, and generative AI products.

Signal
AI → Product

Problem Solving

Consistent practice of data structures, algorithms, and computational problem solving.

Signal
500+ Problems

Full-Stack Product Engineering

Building scalable web applications and SaaS products with modern frontend, backend, database, and deployment technologies.

Signal
Idea → Deployment
Journey

A roadmap of growth

2024–2028
Education

B.Tech Computer Science & Engineering at RKGIT, building strong foundations in software engineering, algorithms, and artificial intelligence.

AI ENGINEERING
Skills

Machine learning, deep learning, NLP, generative AI, LLM applications, and production-oriented AI development.

BUILDING
Projects

Applying AI and software engineering to customer analytics, brand intelligence, and SaaS-based campus recruitment.

PROBLEM SOLVING
Achievements

500+ DSA problems solved, continuous technical learning, project development, and exploration of open-source engineering.

NEXT
Opportunities

Open to AI/ML internships, software engineering opportunities, research, product collaborations, and ambitious technical projects.

Skills

An arsenal for intelligence

Tools, frameworks, technologies, and concepts I use to transform ideas and data into working software systems.

PythonTensorFlowPyTorchPandasNumPyOpenAILangChainReactNext.jsSupabaseSQLGitGitHubPythonTensorFlowPyTorchPandasNumPyOpenAILangChainReactNext.jsSupabaseSQLGitGitHub
Category 01

Machine Learning

  • ·Mathematics for AI
  • ·Statistics
  • ·Probability
  • ·Linear Algebra
  • ·Calculus
  • ·Central Limit Theorem
  • ·Regression
  • ·Classification
  • ·Feature Engineering
  • ·Model Evaluation
  • ·XGBoost
  • ·Scikit-Learn
  • ·SHAP
  • ·Pandas
  • ·NumPy
  • ·Kaggle
Category 02

Deep Learning

  • ·Neural Networks
  • ·CNNs
  • ·RNNs
  • ·LSTMs
  • ·Transformers
  • ·PyTorch
  • ·TensorFlow
  • ·Keras
  • ·Model Training
  • ·Optimization
  • ·Representation Learning
Category 03

Generative AI

  • ·Large Language Models
  • ·Natural Language Processing
  • ·Retrieval-Augmented Generation
  • ·Agentic AI
  • ·GANs
  • ·OpenAI APIs
  • ·Prompt Engineering
  • ·LLM Applications
  • ·Cursor AI
  • ·Claude
  • ·GitHub Copilot
Category 04

Tools & Technologies

  • ·Next.js
  • ·TypeScript
  • ·React.js
  • ·JavaScript
  • ·HTML
  • ·CSS
  • ·Flask
  • ·Streamlit
  • ·Supabase
  • ·PostgreSQL
  • ·SQL
  • ·NextAuth.js
  • ·Git
  • ·GitHub
  • ·Docker
  • ·Kubernetes
  • ·Vercel
Selected Work

Engineering ideas
into real products

Selected machine learning, AI, data, and SaaS projects focused on solving practical problems.

01Machine Learning · Explainable AI

Customer Churn & Campaign Response Predictor

Built an XGBoost machine learning system for predicting customer churn and campaign response using more than 45,000 customer records.

Integrated SHAP explainability to translate complex model predictions into insights understandable by non-technical stakeholders.

Developed an interactive Streamlit analytics dashboard highlighting high-value customer segments and helping prioritize campaign targeting.

PythonXGBoostSHAPStreamlit
45K+
Customer Records
89%
Accuracy
0.85
AUC-ROC
View ProjectGitHub →
churn-analyzer / overview
Customer Risk Overview
Updated 12m ago
At Risk
6,412
High-Value
1,208
Retained
37,980
Churn Probability Distribution
Top SHAP Drivers
  • contract_month_to_month
  • tenure_lt_6mo
  • support_tickets
  • price_hike
SegmentCustomersChurn %LTV
Premium · Long tenure1,2086%$2.4K
Growth · Short tenure3,41034%$820
Basic · Price sensitive2,86041%$390
02NLP · Transformers · Topic Modeling

Brand Sentiment & Trend Analyzer

Fine-tuned DistilBERT for sentiment classification using more than 20,000 customer reviews and achieved a 91% F1-score.

Applied BERTopic to automatically discover emerging complaint themes, praise patterns, and topic shifts from unstructured feedback.

Developed a Brand Health Monitor dashboard for tracking sentiment trends and topic movement over time.

PythonDistilBERTBERTopicStreamlit
20K+
Reviews
91%
F1 Score
Real-Time
Trend Monitoring
View ProjectGitHub →
brand-health / monitor
Brand Health
Overall Score · 78 / 100
▲ +6 wow
Positive
62%
Neutral
24%
Negative
14%
Sentiment Trend30 days
Emerging Topics
onboarding delaypricing claritymobile UXsupport qualityfeature X
Complaint Themes
  • billing questions
  • setup friction
  • docs gaps
03B2B SaaS · Full-Stack Engineering

PlaceSync

SaaS-Based Campus Recruitment Platform

Client
RKGIT
Year
2026

Built and deployed PlaceSync, a multi-tenant SaaS placement platform designed to streamline campus recruitment operations with dual-role access for administrators and students.

Built a student dashboard supporting job discovery, eligibility-based applications, and real-time application tracking; designed an administrative platform for job posting, applicant management, and exportable reports.

Implemented secure role-based authentication using NextAuth.js and optimized backend APIs for scalable multi-user performance. Positioned as a B2B SaaS product with RKGIT as the first paying institutional customer.

Next.jsTypeScriptSupabaseNextAuth.jsPostgreSQLVercel
Multi-Tenant
Architecture
Dual-Role
Access Control
PostgreSQL
Scalable Data Layer
View Live ProductCase StudyGitHub →
placesync.app / admin
Placement Overview
2025–26 season
Active Students
1,842
Applications
6,318
Companies
74
Placement Rate
68%
Hiring PipelineQ3
Applied
1240
Screened
820
Interview
410
Offer
180
Joined
132
Recent Applications
  • A. SharmaSoftware Eng.Interview
  • R. VermaData AnalystApplied
  • M. IyerCloud Eng.Offer
  • N. KapoorML InternScreened
Problem Solving

Strong systems begin
with strong fundamentals.

Consistent practice in data structures, algorithms, and computational problem solving strengthens the way I design, optimize, and reason about software systems.

500+
Problems
Solved
Topics
Arrays & StringsLinked ListsStacks & QueuesBinary TreesBinary Search TreesGraphsDynamic ProgrammingGreedy AlgorithmsBacktrackingHeapsTries
LeetCode ↗GeeksforGeeks ↗Coding Ninjas ↗
vaibhav.lab / problem-solving
Practice Activity
Problem-solving across platforms
▲ Active
Activity · last 20 weeks
Difficulty Split
Easy 38%Medium 44%Hard 18%
Recent Topics
GraphsDPTreesBinary SearchHeapsBacktracking
Algorithm Playground
Dijkstra Shortest Path
Source A → Target H
423215344253A0B∞C∞D∞E∞F∞G∞H∞
Time Complexity
O((V + E) log V)
Space Complexity
O(V)
Applications
GPS · Network Routing · Maps
Contact

Let's build something
intelligent.

Have an AI project, internship opportunity, research idea, software product, or ambitious engineering challenge? Let's talk.

Email
vaibhavgupta0399@gmail.com
Phone
+91 80769 95131
Availability

Open to internships, AI/ML opportunities, research, and technical collaborations.

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