About
With a strong foundation in software engineering and a specialization in machine learning, I bring a deep passion for building intelligent, scalable systems that solve real-world problems. My core expertise spans full-stack development, cloud-native architecture, and DevOps, reinforced by hands-on experience deploying ML models and optimization algorithms in production environments.
I engineered robust cloud-based applications using Python (FastAPI, Django), Node.js, and AWS, deploying predictive models for energy pricing and EV charging optimization with techniques like ARIMA, exponential smoothing, and moving averages. I also developed scalable backend systems, leveraging containerized microservices, CI/CD pipelines (Jenkins), and AWS ECS with Fargate to ensure high availability and rapid deployment.
In parallel, I’ve led the development of multi-vendor full-stack applications featuring AI-driven personalization engines and real-time analytics dashboards. These solutions, powered by modern frameworks such as Next.js, Express.js, and DynamoDB, reflect my commitment to performance, security (OAuth 2.0, JWT, IAM), and user-centric design.
My mission is to continue building production-grade systems at the intersection of DevOps, full-stack development, and data science. Delivering impact through automation, intelligent services, and resilient architecture.
Languages
My favorite languages for software engineering and machine learning.
ML F/W & L
My Go-To Frameworks and Librarys for Designing, Developing, Training, and Deploying ML Models
Full Stack
My preferred technologies for full stack web programming and database architecture.
Go-To Tools
My favorite tools for version control, code editing, and container orchestration.
