I started my journey in data science, leveraging my background in machine learning and analytics to extract insights from complex datasets. Over time, I transitioned into cloud engineering, where I now design scalable, efficient cloud architectures. With expertise in both fields, I bridge the gap between data-driven applications and cloud infrastructure, ensuring seamless integration of AI/ML solutions into enterprise environments.
I’m a 6x AWS-certified professional working at the intersection of AI/ML and Cloud Engineering. My current focus is on building AWS-powered AI solutions, creating MLOps pipelines, and developing open source R and Python packages for knowledge retrieval, context management, and generative AI workflows.
- AI/ML Engineering & Applied Data Science
- Cloud Solutions (AWS: EC2, S3, Lambda, SageMaker, Bedrock, etc.)
- MLOps & DevOps for AI/ML workflows
- R & Python for analytics, automation, and applied ML
- Open source collaboration & knowledge sharing
I’ve contributed to projects outside my own repositories, including:
- MDRCNY/kbretrieveR — R package for retrieving and integrating AWS Bedrock Knowledge Bases into R workflows.
- MDRCNY/documentoR — AI-assisted documentation for R projects.