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Achievement: Pair Extraordinaire

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Achievement: Pair Extraordinaire

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  • Pro

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@Fitter-Tech

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saaim12/README.md

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💫 About Me

Hi, I’m Saaim. I build software that keeps working when a lot of people use it.

  • Open to collaborating on data pipelines, RAG systems, and Django/DRF backends.
  • Going deep on Apache Spark, distributed data processing, and data warehouse design.
  • Ask me about Django REST Framework, RAG (pgvector, multilingual embeddings), Kafka, ETL, and AWS.
  • Fun fact: I reached production-level Django proficiency in a short time through intensive hands-on projects.

🔗 Portfolio: saaim  |  📄 Resume: resume

🌐 Connect With Me

Portfolio LinkedIn Medium Discord Instagram

🚀 Featured Projects

🔹 Real-Time E-Commerce ETL Pipeline

Streaming + batch pipeline: Kafka → Spark Structured Streaming → Medallion (bronze/silver/gold) → PostgreSQL star schema, orchestrated with Airflow. Exactly-once ingestion, dimensional modeling, Dockerized. Kafka Spark Airflow PySpark PostgreSQL Docker

🔹 Multilingual Multi-Tenant RAG Engine

Production-style RAG backend with tenant isolation (JWT claims), per-tenant quotas, multilingual retrieval (multilingual-e5-base), and pgvector search. Next.js frontend. Django REST pgvector JWT Next.js PostgreSQL

🔹Production Grade Recommender SSystem

Flask,Machine Learning,Python

🔹 Single-Tenant English RAG System

RAG backend using pgvector + Google Gemini for grounded document Q&A over a Django API. Django pgvector Gemini

📖 Full write-ups on Medium · code on the repos below.

💻 Tech Stack

Languages
Python Java JavaScript TypeScript C++ HTML5 CSS3

Data Engineering
Apache Kafka Apache Spark Apache Airflow Snowflake Pandas NumPy

Backend
Django FastAPI Flask Node.js Express NestJS JWT

Frontend
React Next.js Angular Redux Vite Chakra

Databases
Postgres MySQL MongoDB Redis SQLite Supabase

Cloud & DevOps
AWS DigitalOcean Google Cloud Docker Firebase Vercel Git GitLab

AI / ML
TensorFlow Keras scikit-learn Matplotlib

Testing & Tools
Jest Playwright Jira Notion Figma

📊 GitHub Stats

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✍️ Random Dev Quote

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  1. AutoEncoders_based_encryption_training_and_fitting_in_devices_such_as_esp_or_rasberry-pi AutoEncoders_based_encryption_training_and_fitting_in_devices_such_as_esp_or_rasberry-pi Public

    trained model using autoencoders then deploying it on flask server and also fitting saved model in the esp or any edge device

    C

  2. sdk-for-customer-facing-channels sdk-for-customer-facing-channels Public

    Forked from expertflow/sdk-for-customer-facing-channels

    Javascript SDK for Chat, WebRTC Audio, and Video Calls that will land on Cisco Contact Center Agents handling calls using Cisco Jabber or similar.

    JavaScript

  3. DSA-Python DSA-Python Public

    A comprehensive repository for prepration of Data Structures & Algorithms through clean, well-documented Python implementations of mine with notes

    Python

  4. Recommender-System Recommender-System Public

    This project implements a production-ready end-to-end recommender system designed to process user interaction data, train recommendation models periodically, and serve personalized recommendations …

    Python

  5. Multi-Tenant-Multi-Lingual-Retrieval-Augmented-Generation-RAG-System Multi-Tenant-Multi-Lingual-Retrieval-Augmented-Generation-RAG-System Public

    Multilingual, multi-tenant RAG engine in Django multilingual-e5 embeddings, pgvector, JWT-isolated tenants, modular prompts, and per-tenant quotas.

    Python

  6. ETL-Pipeline-with-Batch-Straming-Pipelines ETL-Pipeline-with-Batch-Straming-Pipelines Public

    Real-time + batch ETL pipeline for e-commerce order analytics using Kafka, Spark Structured Streaming, Airflow, and a star-schema warehouse. Follows medallion architecture (bronze/silver/gold) on S…

    Python