Machine Learning Engineer

Containerized and deployed applications with Docker and Kubernetes, improving deployment efficiency by 50%. Automated system maintenance tasks using Python and Bash, reducing manual operational work by 40%.

Optimized backend systems and cloud architecture to reduce response times by 20% and improve infrastructure scalability. Contributed to production-grade backend architecture supporting machine learning model training and inference workflows.

Migrated more than 15 applications to Kubernetes, achieving 99.9% uptime and standardizing infrastructure across core services. Built and optimized CI/CD pipelines with GitHub Actions and Jenkins, reducing deployment time by approximately 70%.

Implemented monitoring with Prometheus, Grafana, and SigNoz, cutting incident response time by 50%. Deployed an AI chatbot that improved user interaction efficiency by 35% and reduced dataset preprocessing time by 25%, while advancing production ML inference workflows and engineering reliability.

Designed scalable backend systems using PostgreSQL and REST APIs, increasing data-processing efficiency by 30%. Built cloud-based solutions that raised system uptime to 99.9% and improved application response performance by 20%.

Contributed to CI/CD automation, GitHub Workflows, and Nginx configuration, reducing load times by 20% and deployment times by 50%.

Led engineering initiatives, software architecture, and product development for a platform serving more than 500 active users. Improved page load time by 25% through Next.js optimization.

Improved database accuracy by 20% through NLP data cleaning and built KPI dashboards that accelerated business decision-making by 30%.

Developed a diabetes prediction model with more than 85% accuracy using medical datasets containing over 5,000 patient records.

Created a web interface for real-time predictions, enabling healthcare staff to make faster, data-driven decisions.

Undergraduate studies in Computer Science.