I am a Senior MLOps and Machine Learning Engineer specializing in deploying robust, production-ready AI solutions for mid-to-large-scale enterprises. My primary focus is bridging the gap between data science prototypes and reliable, scalable production systems that drive measurable business value.

If your AI project is stuck in a notebook or struggling to handle real-world data loads, I am the engineer who will design, build, and deploy the automated infrastructure to make your models profitable.

My services are centered on high-impact, full-lifecycle ML engineering:

Computer Vision (CV) System Development: Designing and fine-tuning state-of-the-art models (YOLO, Mask R-CNN) for automated quality control, inventory management, defect detection, and specialized image analysis.

MLOps & Production Deployment: Implementing continuous integration/continuous delivery (CI/CD) pipelines using Docker, Kubernetes, and Terraform to automate model retraining, testing, and deployment across major cloud providers (AWS, Azure, GCP).

Scalable Architecture Design: Architecting highly performant data and inference pipelines using technologies like FastAPI to ensure low-latency model serving that scales seamlessly with your user base.

Model Optimization & Cost Reduction: Refactoring existing models and infrastructure to reduce cloud compute costs and improve inference speed without sacrificing accuracy.