Machine Learning Engineer

🚀 AI-Powered Job Finder API (Open Source!)

Hey everyone! 👋

I'm excited to share an intelligent job search API that uses AI agents to find and recommend the best jobs for you.

What it does:

✨ Searches LinkedIn & Glassdoor simultaneously
🤖 Uses AI (LangGraph + Groq LLM) to analyze and score jobs
🎯 Returns top 10 personalized recommendations with match scores
⚡ Built with FastAPI for blazing-fast performance

Tech Stack:

FastAPI—Modern async Python API
LangGraph—Agentic workflow orchestration
Groq LLM—Free, fast AI analysis (Llama)
BrightData—Real-time web scraping
Pydantic - Type-safe schemas

Why it's cool:
Instead of scrolling through hundreds of job posts, just send one API request and get AI-curated matches with explanations of why each job fits your criteria. The AI agent handles everything: scraping, analyzing, scoring, and ranking.

🔗 GitHub Repo: https://github.com/rooneyrulz/agentic-job-finder

Follow Machine Learning Engineer for more interesting ❤️

GitHub - rooneyrulz/agentic-job-finder: AI-powered job search API using FastAPI, LangGraph agents & Groq LLM. Scrapes LinkedIn/Glassdoor jobs via BrightData and returns intelligent, ranked recommendations with match scoring. AI-powered job search API using FastAPI, LangGraph agents & Groq LLM. Scrapes LinkedIn/Glassdoor jobs via BrightData and returns intelligent, ranked recommendations with match scoring. - rooney...

🚀 Want to become a real AI Engineer?

Start by mastering these 7 RAG (Retrieval-Augmented Generation) fundamentals.

RAG is one of the most practical skills in modern AI engineering. It gives LLMs access to fresh, private and factual information—things they were never trained on.

Let’s break it down. 👇

1️⃣ Why RAG matters

LLMs are powerful, but they come with limitations:

Knowledge cutoff → They stop learning after training
No private data → They can't see your internal docs
Hallucinations → They confidently make things up

RAG solves these issues by grounding every answer in retrieved, verified context.
When your company updates a policy, you just update the data—not the model.

2️⃣ The RAG Pattern (Simple but powerful)

RAG follows a clean loop:

1. Retrieve relevant context from your knowledge base
2. Augment the prompt with that context
3. Generate an answer grounded in real data

This pattern works across apps, bots, copilots, and enterprise systems.

3️⃣ Clean data = Better RAG

If your documents are messy, unstructured, or full of noise — even the best embeddings will return garbage.

📌Clean, well-formatted data is the easiest multiplier of RAG accuracy.
Poor data → poor retrieval → poor answers.

4️⃣ Retrieval quality is everything

RAG doesn’t fail at generation — it fails at retrieval.

Too few documents → missing context
Too many → noise
Wrong documents → confident nonsense

Always debug retrieval first.
If retrieval is bad, generation will *always* be bad.

5️⃣ Retrieval methods (RAG ≠ vector DB)

A big misconception: RAG is not about vector databases.
It’s a pattern. Retrieval can be as simple as loading text files.

Use the simplest approach that works:

🔎 Keyword search → exact matches
🧠 Semantic search (embeddings) → meaning-based retrieval
🔀 Hybrid search → keyword + semantic
🤖 Agentic search → LLM decides what to retrieve

6️⃣ Embeddings: The backbone of semantic search

Embeddings turn text into vectors like:
"How do I reset my password?" → [0.23, -0.45, 0.67, ...]

Sentences with similar meaning cluster together:

“I forgot my password”
“Need help resetting my password”

That’s how semantic search retrieves accurate context even when wording differs.

7️⃣ Chunking: What the model actually sees

Chunking splits documents into smaller pieces for retrieval.

Too small → missing context
Too large → irrelevant noise

📌 Best practice:
Split by logical sections, with 50–100 token overlap to avoid context loss.

Chunking directly shapes what the model sees—so it shapes your final output.

🎯 Final thought

RAG isn’t a product or a framework—it's a design pattern.
A pattern for giving LLMs the right context at the right time so they produce accurate, reliable answers.

If you want to build AI systems that work in the real world, start by mastering RAG.

learn AI the smart way ⭐
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🚀 Exciting Machine Learning Project Update! 🚀

🔍 Project Overview: Heart Disease Binary Classifier

We are thrilled to share the latest updates on my machine learning project! 🤖📊 This project focuses on developing a binary classifier for predicting heart disease, leveraging Python and Scikit-learn for a comprehensive ML journey.

🌐 Key Features:

1. Data Preprocessing and Cleaning: Ensuring data integrity and enhancing predictive capabilities.

2. Exploratory Data Analysis (EDA): Gaining valuable insights through analysis and visualization.

3. Binary Classification Model: Implementing Logistic Regression, K-Nearest Neighbors and Support Vector Machine for comparative evaluation.

4. Hyperparameter Tuning: Fine-tuning the model for enhanced predictive accuracy.

5. Cross-Validation and Grid Search: Ensuring robustness across different subsets of the dataset.

💻 Technologies Used:

Python
Scikit-learn

📚 Note:

This project is designed for experimentation and learning purposes, not for production use. Feel free to explore the codebase, experiment with parameters, and contribute to ongoing development.

🤝 Your Input Matters:

We invite you to check out the GitHub repository, read about the project, and share your thoughts. Your contributions and feedback are highly welcomed! Let's learn and collaborate together. 🌟

Link to GitHub Repository 👉 https://github.com/rooneyrulz/heart-disease-binary-classifier

Happy Coding! 🚀

Handling Imbalanced Data.

Data Mining ⛏️

Learn how to build a fully integrated and cookie-based authentication application programming interface (API) that can be consumed with any frontend client web or mobile (React.js, Angular, Vue, Svelt, ....)

Teach used:

Node.js
Express.js
MongoDB
JavaScript (ES6)
JWT
Docker
Custom middlewares
Password Hashing

Learn how to write clean code :)

Feel free to get the complete source code via inbox.
Happy coding! 🙂

MLOps 👍

💯% Free


Let's build your own multiple role-based authentication systems with privileges based on the user roles.

Teach used:
React.js
Typescript
Material UI
Redux
Redux-thunk
React-router
Formik

Node.js
Express.js
Json-web-token
Custom-auth-middlewares
MongoDB

Docker
Docker-compose
Containerization

Feel free to get the source code via inbox.

Happy coding! 🙂