Aamir Hussain
Azad
I transform data into intelligent products — combining machine learning, analytics, APIs and modern interfaces to solve real business problems.
🎓 B.Tech CSE · AI & ML
⭐ 8.6 CGPA
At my desk and with the team
Crop Yield Prediction System
A clear view of how agricultural inputs move through preprocessing and regression models to produce a yield estimate and evaluation charts.
Technology I use from data to deployment
Three skill tracks — AI/ML and data, engineering tools, and deployment — followed by my toolkit and the AI backend syllabus I’m working through. Hover a track to pause it.
Media introduction & project demos
Hear a short voice introduction or watch a project walkthrough. Drop an audio or video file onto a card to preview it instantly — files stay private in your browser.
Listen to a short introduction to my work in AI/ML, data analytics, and full-stack development.
Object detection workflow
REST API structure
K-means RFM customer groups
Regression model metrics
Automated data cleaning
Audio
Video
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I connect machine learning, data, and product development.
I'm Aamir Hussain Azad, an AI/ML Engineer at Root Developers, Gurugram. I build object detection, image generation and crop yield prediction models, and ship them inside full-stack MERN web apps so people can actually use them.
- 💼Current roleAI/ML Engineer · Root Developers
- 🎓EducationB.Tech CSE (AI & ML) · UEM Jaipur · 8.6 CGPA
- 📊Experience2 yrs data analytics · Delta 360 Services
- 📍Based inGurugram, Haryana, India
Before ML engineering I spent two years in data analytics at Delta 360 Services — cleaning data, running SQL and Pandas analysis, and building Excel and Power BI dashboards that teams used to make decisions. During my internship at OctaNet I built responsive pages, HubSpot forms and Mautic CRM integrations, and raised the site's Google web-performance score by about 50 points.
My strongest work starts with a practical problem: understand the data, build a reliable solution, evaluate it honestly, and present the result in a form people can act on. Outside work I automate data pipelines with Python and keep my notes and practice public in Aamir CodeHub.
AI/ML ProjectMachine learning, data analytics and full-stack web apps — from raw data to a deployed, explainable result.
Technical strengths
AI/ML engineering
Data cleaning, exploratory analysis, feature engineering, regression, classification, neural-network foundations, and honest model evaluation.
Data analytics
SQL analysis, Excel, Power BI dashboards, statistics, KPI thinking, and translating data into understandable insights.
Product development
Responsive React interfaces, Node/Express APIs, authentication, MongoDB, CRM-connected forms, and complete application workflows.
Where I’ve worked
- Current · GurugramAI/ML Engineer — Root Developers
Object detection, image generation and crop yield models, integrated into MERN web apps; Python automation for data and content pipelines.
- 2 yearsData Analytics — Delta 360 Services Pvt. Ltd.
Data cleaning, EDA, SQL reporting, customer and product analysis, KPI tracking and Excel / Power BI dashboards.
- Nov 2023 – Apr 2024 · 6 months · JaipurFrontend Developer Intern — OctaNet
Responsive pages, HubSpot forms, Mautic CRM integration and web-performance work.
- 2023 – 2024 · UEM JaipurB.Tech major project — Crop Yield Prediction
End-to-end regression pipeline evaluated with MAE, RMSE and R².
Highlights & recognition
Languages, frameworks and tools I work with
Grouped the way I use them on real projects — from the language layer down to the concepts underneath.
Languages
Frontend
Backend
Database
Data & ML tools
Concepts
AI backend integration — complete syllabus
The path I follow to build production AI backends: from Python fundamentals to secure APIs, machine learning, LLM applications and deployment — 9 modules, each finished with a real project.
- Concept
- Syntax
def& code examples- Real-life use case
- Hands-on practice
- Testing
- Project integration
Python Programming
def)LambdaModulesOOPExceptionsFile handlingType hintsDecoratorsGeneratorsUsed for: Clean, reusable code that every service below is built on.
Developer Tools
pipDebuggingUnit testingUsed for: Version-controlled, reproducible and tested projects.
Database & Data
Used for: Store, query and prepare the data that models learn from.
Backend Development
Used for: Serve models and data to apps through clean, documented APIs.
Advanced Backend
Used for: Secure, fast services that hold up under real traffic.
Machine Learning & AI
Used for: Predictions and insights on structured business data.
Deep Learning & Generative AI
Used for: Chatbots, semantic search and assistants grounded in your own documents.
Deployment & MLOps
Used for: Ship models to production and keep them healthy over time.
Real Projects
Used for: Capstones that combine every module into portfolio-ready products.
How I turn data into decisions
Core areas from my B.Tech (AI & ML) coursework and applied in projects like the crop yield predictor — from raw data to a working, evaluated model.
Machine learning is just teaching a computer to spot patterns in past data so it can make useful predictions about new situations. In my crop yield project, that means: feeding it years of weather and soil records, letting it learn which conditions lead to a good or bad harvest, and then asking it to predict next season's yield — so farmers can plan better. The five steps below show how any of my ML projects go from raw data to a working prediction.
AI/ML areas I work in
Six areas, each tied to real work or a project you can open on this site.
Computer Vision
Teaching models to find and label objects in images and video.
Generative AI
Models that create new images and text from a prompt or example.
NLP & LLM apps
Understanding and answering questions in natural language.
Predictive ML
Learning from historical records to forecast an outcome.
Data analytics
Turning raw data into KPIs, segments and decisions.
Deploying ML
Wrapping models in apps and APIs people can actually use.
What I build models with
Where AI is heading — and where I’m heading with it
AI is moving from single predictions to systems that see, reason and act. These are the shifts I’m preparing for, and how they connect to my work.
Agentic AI
LLMs that plan, call tools and APIs, and finish multi-step tasks — the reason I’m learning FastAPI, tool calling and agents.
Multimodal models
One model that reads text, images, audio and video together — a natural next step from my vision and generative work.
Edge & on-device AI
Small, fast models running on phones and cameras, so predictions work offline and keep data private.
AI for agriculture
Yield forecasting, crop advice and resource planning for farmers — the problem behind my B.Tech project.
AI in health & wellbeing
Early screening and wellness tracking — the space my Mansik Santulan app explores.
Responsible AI
Fair, explainable and privacy-aware systems that people can trust with real decisions.
- Start from the real problem, not the model
- Evaluate honestly on held-out data
- Explain results in plain language
- Protect user data and privacy
- Watch for bias and drift after launch
AI/ML, data analytics & product experience
I currently work as an AI/ML Engineer at Root Developers, Gurugram. My experience also combines two years of Data Analytics work at Delta 360 Services Pvt. Ltd. with end-to-end AI/ML project development, production frontend work, and continuous software engineering projects.
- Build object detection, image generation and crop yield prediction models.
- Integrate the models into full-stack MERN web applications so they are usable end to end.
- Automate data pipelines and content workflows with Python.
- Used Python, Pandas, NumPy and SQL for data preparation, validation, aggregation, joins, filtering and exploratory analysis.
- Built KPI-driven analytics views covering revenue, profit, customer behaviour, product performance, returns and operational trends.
- Applied Excel and Power BI concepts including Power Query, calculated measures, dashboard layouts and business-focused visual storytelling.
- Practised statistical summaries, correlation analysis, customer segmentation, RFM-style analysis, outlier checks and data-quality reporting.
- Documented findings so technical analysis could be understood by recruiters, stakeholders and non-technical users.
- Redesigned and maintained production webpages using HTML, CSS, and JavaScript with responsive, mobile-first layouts.
- Created new BBA, CLAT, and GMAT pages and contributed to a new-domain website while keeping visual patterns consistent.
- Integrated HubSpot forms and Mautic CRM to support lead capture and follow-up workflows.
- Improved page structure, usability, responsiveness, and web-performance practices, increasing the Google web-development score by approximately 50 points.
- Combined soil, weather, location, crop, season, area, and historical production features into a model-ready dataset.
- Handled missing values, duplicate checks, categorical encoding, numerical scaling, exploratory analysis, and feature engineering.
- Compared classical regression and neural-network approaches instead of depending on a single algorithm.
- Used MAE, RMSE, R², residual checks, and actual-versus-predicted plots to evaluate model behaviour.
- Presented technical output as a decision-support concept for yield planning, resource allocation, and productivity analysis.
- Strengthened Python, SQL, statistics, data analytics, machine learning, Java, and DSA through structured practice.
- Built practical applications including listing platforms, REST APIs, analytics dashboards, and ML prototypes.
- Practised complete workflows: problem definition, data preparation, implementation, evaluation, documentation, and presentation.
Resume & career documents
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Resume, cover letter & credentials
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Resume
Complete AI/ML resume preview with data analytics experience, projects, skills and education.
Cover Letter
Professional general-purpose AI/ML and data cover letter with optional PDF, Word, text or image replacement.
Experience Certificate
Employment summary for Root Developers, Delta 360 and OctaNet. Upload the official certificate to show it here.
Academic Details
B.Tech (AI & ML), UEM Jaipur · Enrollment no. 12020002001161. Upload the official ID to show it here.
Aamir Hussain Azad
Dear Hiring Manager,
I am writing to apply for an AI/ML Engineer, Machine Learning or Data role in your team. I currently work as an AI/ML Engineer at Root Developers in Gurugram, where I build object detection, image generation and crop yield prediction models and integrate them into full-stack MERN web applications.
Before this, I spent two years in data analytics at Delta 360 Services Pvt. Ltd., cleaning and analysing data with Python, Pandas and SQL, building KPI dashboards in Excel and Power BI, and turning findings into clear recommendations for non-technical teams. During my internship at OctaNet, I built responsive pages, connected HubSpot and Mautic CRM forms, and improved site performance by about 50 points on Google’s score.
My projects show the same end-to-end approach: EstateIQ, a real estate price predictor with a FastAPI back end and secure login; Arvixa, a multi-seller marketplace with sales forecasting, churn prediction and a RAG assistant; and a crop yield prediction system from my B.Tech (AI & ML) at UEM Jaipur, where I graduated with a CGPA of 8.6.
I would welcome the chance to bring this mix of machine learning, data analysis and product development to your organisation. Thank you for your time and consideration — I would be glad to discuss the role at your convenience.
Yours sincerely,
Aamir Hussain Azad
Aamir CodeHub
Connected · Learning workspaceOpen my redesigned AI/ML + DSA learning workspace with machine-learning notes, algorithms, Striver practice, projects, dry runs, interview preparation and engineering tools.
Projects presented as real case studies
Each project explains the problem, workflow, features, technology, evaluation approach, and practical value—not only the tools used.
Agricultural Production & Crop Yield Prediction System
An end-to-end machine-learning system designed to estimate crop yield from soil conditions, weather patterns, location, crop type, and historical production records. The goal is to turn scattered agricultural data into understandable planning insights.
- Missing-value treatment, duplicate checks, encoding, scaling, and exploratory data analysis.
- Feature engineering to expose relationships between climate, soil, crop selection, and yield.
- Regression and neural-network model comparison with a clear evaluation workflow.
- Charts for trend analysis and a decision-support view for resource and crop planning.
EstateIQ — Real Estate Price Predictor
Enter a property’s city, locality, size, rooms, floor, age and amenities, and get an estimated price plus the features that drove it — behind a secure login.
- scikit-learn pipeline: imputation, one-hot encoding and a Random Forest regressor, evaluated with MAE, RMSE and R².
- Valid cities and localities load from the API, so users can only pick supported inputs.
- Sign-up and login with PostgreSQL and HttpOnly JWT access/refresh cookies — no tokens in local storage.
- Runs with Docker Compose (PostgreSQL); a RAG/GenAI module is part of the backend.
Arvixa — Online Shopping Marketplace
A multi-seller online store: shoppers search, add to cart and pay; sellers manage products and orders in a Seller Hub with sales forecasts, churn and best-seller predictions.
- Shopper flow: search with suggestions, filters, 360° product view, cart, wishlist, 4-step checkout with Razorpay (UPI, cards, net banking) or COD, order tracking.
- Seller Hub: product CRUD with photos and video, bulk edits, order fulfilment, earnings and CSV exports.
- ML trained on the shop’s own data: 30-day sales forecast, best-seller prediction, RFM + K-Means segments, churn model, “recommended for you”.
- AI assistant with RAG answers, and support tickets triaged by a TF-IDF + logistic regression classifier.
- Docker Compose locally, AWS deployment templates (ECS, RDS, S3) and GitHub Actions CI with tests.
Crop yield prediction system
Predicts agricultural yield from environmental and historical signals, then presents the result through interpretable evaluation visuals.
- Cleaned, encoded, scaled, and explored agricultural records.
- Compared models and visualized actual-versus-predicted output.
- Designed for crop planning and resource-allocation insights.
Airbnb clone
A full-stack listings app with authentication, CRUD operations, and a responsive booking-style interface backed by a RESTful API.
- Supports listing creation, viewing, updating, and deletion.
- Connects React screens to Express APIs and MongoDB persistence.
- Uses responsive layouts and reusable components across devices.
Zerodha clone
A trading-platform clone with user auth and interactive UI components, built to practice real-world full-stack patterns end to end.
- Builds a dashboard-style interface with reusable React components.
- Connects authentication and application data through backend APIs.
- Practises full-stack structure, routing, state, and database flow.
Smart Email Spam Classifier
Trains a text-classification model to detect spam messages, then wraps it in a small Flask web app for live predictions.
- Cleans and tokenizes email text before TF-IDF feature extraction.
- Compares Naive Bayes and SVM-style classification approaches.
- Returns a spam/ham prediction through a simple Flask interface.
AI-Powered Chatbot (RAG)
A conversational AI assistant that retrieves verified context from a curated document store before generating each answer — grounding every response in real source material. Connected directly to Aamir's portfolio: ask it anything about his projects, skills, or experience.
Connected to aamirhussainazad786 portfolio ↗Professional Email Checker
A professional-grade email validation tool that checks syntax, domain existence, disposable email detection, and MX record verification — giving instant pass/fail feedback.
- Validates email format, domain, and MX records in real time.
- Detects disposable/throwaway email providers automatically.
- Clean, responsive UI with colour-coded result badges.
Zoom Clone — Video Conferencing App
A real-time video conferencing application built as part of the Apna College curriculum, supporting multi-participant calls, chat, mic/camera controls, and room-based sessions.
- Enables peer-to-peer video/audio via WebRTC and Socket.IO signalling.
- Supports room creation, joining by ID, and participant management.
- Includes live in-call chat and mute/video-off toggle controls.
Mansik Santulan — Mental Wellness Scores
A live mental wellness scoring dashboard that evaluates mood, anxiety, sleep quality, and stress levels, presenting results through interactive visualizations to support self-awareness and tracking over time.
- Scores multiple wellness dimensions and computes an overall health index.
- Presents results through radial gauges, trend lines, and dimension breakdowns.
- Deployed as a live web application — open and usable in the browser.
Retail Sales & Customer Analytics
A live retail analytics dashboard that tracks sales trends and customer behaviour side by side — surfacing revenue movement, order value, and repeat-customer patterns through interactive visuals.
- Visualizes sales trends alongside customer segment breakdowns.
- Surfaces key KPIs — total sales movement and average order value.
- Deployed as a live web application — open and usable in the browser.
Academic background
A B.Tech in Computer Science with an AI & ML specialization, three NPTEL certifications from IITs, and continuous self-learning in DSA, full-stack development and data science.
📜Education timeline
-
2020 – 2024 · Jaipur, Rajasthan
B.Tech, Computer Science & Engineering (AI & ML)
University of Engineering and Management, JaipurSpecialized in Artificial Intelligence and Machine Learning, on top of core computer science. Final-year major project: an end-to-end Crop Yield Prediction System.
CGPA8.6/ 10 -
2018 – 2020 · Bihar
Intermediate (Class 12), BSEB
TP Verma College, BiharHigher secondary education under the Bihar School Examination Board.
Score79.7% -
2018 · Bihar
Matriculation (Class 10), BSEB
Jharmahuwi High School, BiharSecondary education under the Bihar School Examination Board.
Score67.4%