I am a Fullstack Machine Learning Engineer focused on bridging the gap between ML models and production-ready applications. I build end-to-end solutions, from data versioning and model training to scalable API deployment.

• Computer Vision & NLP: Actively exploring and implementing state-of-the-art architectures.
• MLOps: Ensuring reproducibility with DVC and efficient experiment tracking.
• High-Performance APIs: Building lean and fast interfaces for ML model inference.

Languages

Machine Learning & Data Science

Backend & Infrastructure

• (Main choice for model inference)
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Tools & Deployment

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• (Self-hosted local environments)

• Efficiency first: Using FastAPI for lightweight and high-speed model connectivity.
• Data Integrity: Implementing DVC to manage datasets and ML pipelines.
• Asynchronous Processing: Leveraging Celery and Redis for heavy ML tasks and background jobs.

I'm always open to discussing ML architecture, backend optimization, or interesting open-source projects.