AI & Data Engineer | Backend Architecture • Computer Vision • Distributed Systems
I build scalable AI solutions and robust data pipelines from the ground up — bridging the gap between advanced machine learning models (GenAI/Vision) and high-performance backend infrastructures.
🔭 Current Focus: Architecting real-time streaming pipelines and training custom deep learning models for vision tasks.
A comprehensive multi-container data engineering pipeline designed for real-time processing and storage.
FastAPI Apache Kafka Apache Spark (Streaming) MinIO PostgreSQL Redis Docker Compose
An extensive collection of deep learning and computer vision implementations. Highlights include:
- Facial Emotion Recognition: Custom CNN model trained on the FER2013 dataset.
- Image Generation: Deep Convolutional GAN (DCGAN) implementation on the CelebA dataset.
- Advanced Digital Image Processing: Superpixel segmentation, Laplacian pyramids, and homography-based panorama stitching.
PyTorchOpenCVCNNsGenerative AI (GANs)
Asynchronous computer vision inference API featuring image upload, validation, background processing, and scalable task execution for AI workloads.
Python FastAPI Celery Redis Docker
RAG-based document question-answering system that allows users to chat with their own documents. Built end-to-end with document parsing, chunking, embeddings, and vector search.
FastAPI SentenceTransformers ChromaDB
Languages & Package Management: Python (uv, AsyncIO) • JavaScript • TypeScript • SQL
Data Engineering & Backend: FastAPI • Apache Kafka • Apache Spark • PostgreSQL • Redis • MinIO
AI & Computer Vision: PyTorch • OpenCV • Scikit-Learn • Generative Models (DCGANs, Diffusion) • NumPy/Pandas
DevOps & Workflow: Docker & Docker Compose • Git/GitHub • Linux (WSL)
💡 Open to roles in: Data Engineering, Machine Learning Engineering, AI Infrastructure, and Python Backend.