I'm a seasoned Big Data and Data/ML Ops Engineer with extensive experience across various public cloud platforms. My expertise encompasses software development using Java and Python. As a testament to my skills, I've earned multiple certifications: 3 in GCP, 3 in Azure, 2 in AWS, as well as in Oracle, Terraform, and Apache Airflow. Recently, I've expanded my proficiency to include roles as an AWS DevOps Engineer and a GCP Vertex ML Engineer. Driven by curiosity and clear objectives, I excel in adopting and imparting new technologies. My collaborative approach makes me a valuable team member, always eager to learn and share knowledge.
Google Cloud Platform
DevOps
Amazon Web Services
Microsoft Azure
Python Development
Big Data
Bachelor of Computer Engineering
I have experience with GCP Big Data suite and AI Platform. I specialize in crafting machine learning models using Vertex AI. My expertise also lies in the strategic design of robust systems, ensuring they are scalable, resilient, efficient, and fault-tolerant.
I have extensive experience working with both relational and non-relational databases. My expertise includes systems such as Oracle, MySQL, MariaDB, SQL Server, Postgres, and MongoDB, as well as proficiency in BigQuery for big data analytics.
I am actively gaining experience in Python, with a focus on web frameworks like Django and Flask. Additionally, I am delving into various Machine Learning frameworks. Notably, I have contributed as a Python developer to projects within the Data-Zen-Community on GitHub.
My holistic approach to DevOps emphasizes collaboration, monitoring, tool-chain pipelines, and infrastructure as code, which empowers teams to develop, test, and release software swiftly and reliably.
I have robust expertise in Git version control. I'm proficient in collaborative workflows, employing pull requests and code reviews to maintain code integrity.
I have a profound expertise in leveraging LaTeX to craft meticulous scientific documentation.
