Jul 2024 – Present · 1 yr 8 mos

Jan 2024 – Jun 2024 · 5 mos

• • Successfully integrated HashiCorp Vault across all environments for secrets retrieval, thereby enhancing security and effective management of sensitive data within the Infrastructure

Jul 2022 – Dec 2023 · 1 yr 5 mos

• Developed and implemented a highly scalable AWS infrastructure utilizing SQS, Lambda, EventBridge, S3, and ASG, resulting in optimized compute resources and a 50% reduction in processing time.
• Engineered a pull-based architecture for message processing, leveraging the Boto3 library for seamless integration and efficient message management.Containerized the solution using Docker for simplified deployment and scalability across diverse environments.
• Initiated a cost optimization campaign spanning AWS and GCP cloud infrastructures, analyzing resource utilization and requirements to achieve a 40% reduction in expenses.
• Crafted robust Jenkins pipelines to automate and optimize the build and deployment processes, thereby enhancing efficiency, reliability, and continuous integration.
• Automated manual job monitoring by deploying Elasticsearch and Kibana as monitoring tools, complemented by a custom container solution. This initiative significantly minimized human errors and manual monitoring efforts, resulting in a drastic reduction of man-hours from over 5 hours to just 10 minutes.
• Successfully conducted a proof of concept (POC) and seamlessly integrated Filebeat for log retrieval, achieving a remarkable 99% reduction in log loss risk on on-demand virtual machines thereby ensuring data integrity and enhanced system reliability.
• Enhanced system performance by upgrading the existing framework from Flask to FastAPI, resulting in a 30% increase in request processing speed.
• Conducted research and performance evaluation of Uber Orbit and Neural Network models for Time series forecasting.

Aug 2021 – Jun 2022 · 10 mos

• Researched and implemented a JSON schema parsing architecture for custom configurations, resulting in a significant 90% reduction in redundant errors.
• Developed comprehensive unit test cases using Pytest for data science modules, enhancing confidence in the reliability and accuracy of the codebase.