🔭 Currently building a real-time ETL pipeline (Kafka · Spark · Airflow · star-schema warehouse) and AI/data-engineering backends.
👯 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.vercel.app | 📄 Resume: saaim.vercel.app
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
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
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.