• Currently leading the development of a comprehensive network monitoring dashboard powered by Zabbix, utilizing SNMP v2 agents and traps to monitor key performance indicators across multiple network devices for diverse customers.• Designed and developed Python scripts to collect ARP data from various network interfaces, to create detailed project-wide network topologies.• Architected and implemented scalable Python (FastAPI) microservices for a unified observability dashboard, monitoring technologies across various categories, including databases, middleware, networks, OS, and storage.• Designed and implemented a robust activity logging module for monitoring Python microservices, using the decorator pattern and Kafka to capture and stream real-time API activity data.• Developed high-performance Python (FastAPI) microservices to monitor multiple Kubernetes clusters, calculating metrics like CPU and memory usage.• Created and executed Python based batch jobs utilizing high performance Elasticsearch queries to develop aggregated indexes for monitoring critical high-volume index metrics.• Developed Python services to extract metrics and event data from Netscaler load balancers by leveraging Netscaler ADM APIs.• Optimized API response times from 3-4 seconds (up to 30 seconds) to under 500 ms for high data volume and time series APIs.• Guided the team in writing high-performance Elasticsearch queries and managing indexes to improve read times based on business use cases.

• Migrated Legacy ML Models to AWS SageMaker: Facilitated the transition of existing machine learning models to AWS SageMaker for improved inference capabilities.• Hyperparameter Tuning for Video Classification: Enhanced the performance of legacy ML models by optimizing hyperparameters, leading to better video classification outcomes.• Python, AWS Sagemaker, S3, EC2, TensorFlow