Feb 2022 – Jun 2025 · 3 yrs 4 mos · Bengaluru, Karnataka, India

• Designed reusable AWS components to enhance scalability, high availability, and fault tolerance, improving overall system reliability.
• Led the migration of 150+ microservices to AWS EKS, ensuring seamless scalability and performance.
• Developed a multi-version compatible Helm Chart, improving operational efficiency across multiple Kubernetes cluster versions.
• Converted infrastructure into code using Terraformer, enabling automated and version-controlled infrastructure management.
• Built an automated recovery system, reducing downtime and manual interventions.
• Successfully upgraded all production Kubernetes clusters from version 1.24 to 1.30 in AWS EKS with Zero Downtime. Implemented progressive rollouts, node upgrades, and traffic routing strategies to ensure uninterrupted service.
• Optimized Dockerfiles with multi-architecture builds using Buildx and BuildKit caching, improving build efficiency and reducing image sizes.
• Automated Kafka topic management with a Python-based Jenkins integration, streamlining replication and partition adjustments.
• Implemented a custom EFK stack for efficient log aggregation and faster issue resolution.
• Built an end-to-end compliance pipeline integrating Lambda-based auto-tagging with real-time EC2 monitoring, ensuring instant non-compliance alerts.
• Developed an automated domain monitoring system for 400+ domains using GoDaddy APIs, Prometheus, and Grafana, preventing domain expiration.
• Designed a real-time microservices monitoring system with Prometheus & Blackbox Exporter, reducing downtime and improving service availability.
• Integrated dynamic alerting with Google Chat webhooks, ensuring real-time outage detection and faster incident response.

Apr 2020 – Mar 2021 · 11 mos · Jaipur, Rajasthan

• Designed and implemented end-to-end ML production systems, including project scoping, data processing, modeling strategies, and deployment requirements.
• Established model baselines, addressed concept drift, and developed strategies for continuous improvement of production ML applications.
• Built data pipelines for gathering, cleaning, and validating datasets to ensure high-quality inputs for ML models.
• Deployed and maintained ML models on Kubernetes, ensuring scalability, reliability, and efficient resource utilization.
• Implemented saving and loading mechanisms for deep learning models using Keras for efficient model management.
• Developed Computer Vision solutions leveraging Neural Networks for text data processing, image, and video analysis.