Hamza Bilal.

Hello! My name is Hamza Bilal and I'm a passionate Backend Developer and AI Engineer specializing in backend development, machine learning, and AI-powered applications. My journey in programming began during my studies in Data Science, where I discovered the incredible potential of Python for building robust, scalable applications and intelligent systems. Read my technical blog posts for insights.

I specialize in backend development with extensive expertise in Django, Django REST Framework, FastAPI, and Flask. My experience spans across building RESTful APIs, implementing OAuth flows, and creating modular architecture patterns. I'm proficient in SQL databases, Git version control, and comprehensive testing with pytest, ensuring high-quality, maintainable code. Check out my featured projects to see my work.

My passion lies in the intersection of AI and web development. I have extensive experience working with Hugging Face models for text analysis, video analysis, and image analysis projects. I've built AI agents for transcript generation using Agno and OpenAI, and implemented Whisper models for video transcription and analysis. My work includes deploying custom ML models on AWS SageMaker and utilizing AWS Comprehend for advanced text analysis.

I've also worked on data scraping projects, extracting valuable insights from social media platforms using Apify and other advanced techniques. These projects involved comprehensive text and video analysis, leveraging ML models to derive meaningful patterns and trends from large datasets. View my complete project archive.

Social Media Assessment (End-to-End AI/ML System)

I engineered an end-to-end social media assessment pipeline that ingests creator content from TikTok, Instagram, Facebook, and YouTube using Apify crawlers. The pipeline standardizes posts, captions, thumbnails, and videos, then routes them to specialized analyzers:

• TextAnalyzer: AWS Comprehend for sentiment, key phrases, entities, and language.
• ImageAnalyzer: Custom CNN models deployed on AWS SageMaker, consumed via HTTPS endpoints.
• VideoAnalyzer: Video → frames every 3 seconds → batch image analysis → aggregate insights.
• Transcription: OpenAI Whisper for accurate multilingual speech-to-text.
• Agents: Task-specific AI agents orchestrated with Agno + OpenAI for enrichment and reporting.

Python Web Frameworks in My Workflow

I build robust backends with Django for admin, ORM, and authentication; expose clean REST APIs withDjango REST Framework for browsable APIs and permissions; use FastAPI for high-performance, async microservices (model-serving, webhooks); and leverage Flask for lightweight utilities and internal tools. Services communicate over REST/JSON, with background jobs running via Celery/Redis.

ML Stack and Operations

Models and libraries I use include: Transformers and pipelines from Hugging Face, OpenAI APIs, custom TensorFlow/Keras and PyTorch CNNs for image understanding, and Whisper for ASR. I containerize models, deploy to AWS SageMaker (real-time endpoints), and operationalize data flows with S3, CloudWatch, and IAM. Text understanding uses AWS Comprehend for scalable NER, sentiment, and topic signals.

Currently, I'm working as a Backend Developer at Distack Solutions, where I develop and maintain scalable backend services using Python, Django, and Django Rest Framework. I'm passionate about driving innovation through AI integration and continuously improving development processes with cutting-edge technologies.

Here are a few technologies I've been working with recently: