Lavish Gupta

Led the implementation of client infrastructure on EKS, using Terraform to integrate AWS services such as ALB, RDS, Secrets Manager, EC2, ACM, Route 53, CloudFront, and S3 buckets.
Orchestrated the deployment and management of Kubernetes clusters on Amazon EKS, configuring settings, networking, and security policies to meet application requirements.
Optimized cluster performance, scalability, and reliability using resource allocation, Karpenter for autoscaling nodes, KEDA for scaling workloads, and other Kubernetes-native features.
Configured Nginx and ALB Ingress Controllers for efficient traffic routing, SSL termination, and enhanced application availability.
Engineered a 60–70% reduction in EKS operational costs by automating scale-downs, leveraging Spot/Graviton instances, and implementing Karpenter for dynamic node scaling.
Familiarized with Apache Airflow components and UI for workflow orchestration and task management, supporting client’s data pipeline operations.
Implemented Semantic release automation from scratch using GitHub Actions, ensuring consistent versioning and changelog generation.
Integrated these workflows with CI/CD pipelines for seamless code deployment, version control systems, and container registries.
Configured and deployed an end-to-end monitoring stack with Prometheus, Loki, Promtail, and Mimir to gain real-time insights into Kubernetes clusters and containerized workloads.
Created custom Grafana dashboards, alerts, and metrics for proactive performance optimization and incident response.
Developed a Python-based data pipeline to automate the extraction and transformation of Monday.com data into AWS RDS using Docker, Lambda, Kinesis Firehose, S3, and API Gateway.

Led the implementation of client infrastructure on EKS, using Terraform to integrate AWS services such as ALB, RDS, Secrets Manager, EC2, ACM, Route 53, CloudFront, and S3 buckets., Orchestrated the deployment and management of Kubernetes clusters on Amazon EKS, configuring settings, networking, and security policies to meet application requirements. Optimized cluster performance, scalability, and reliability using resource allocation, Karpenter for autoscaling nodes, KEDA for scaling workloads, and other Kubernetes-native features. Configured Nginx and ALB Ingress Controllers for efficient traffic routing, SSL termination, and enhanced application availability., Engineered a 60–70% reduction in EKS operational costs by automating scale-downs, leveraging Spot/Graviton instances, and implementing Karpenter for dynamic node scaling., Familiarized with Apache Airflow components and UI for workflow orchestration and task management, supporting client’s data pipeline operations., Implemented Semantic release automation from scratch using GitHub Actions, ensuring consistent versioning and changelog generation. Integrated these workflows with CI/CD pipelines for seamless code deployment, version control systems, and container registries., Configured and deployed an end-to-end monitoring stack with Prometheus, Loki, Promtail, and Mimir to gain real-time insights into Kubernetes clusters and containerized workloads, creating custom Grafana dashboards, alerts, and metrics for proactive performance optimization and incident response., Developed a Python-based data pipeline to automate the extraction and transformation of Monday.com data into AWS RDS using Docker, Lambda, Kinesis Firehose, S3, and API Gateway.

Contributed to a feature for applying Snapchat-like filters, enhancing user experience.
Implemented CI/CD pipelines using GitHub Actions for automated testing and deployment, ensuring efficient updates.
Utilized OpenCV to develop a face detection algorithm, containerized it using Docker, and deployed it to AWS via GitHub Actions, ensuring seamless integration and performance.
Leveraged the MediaPipe library to create a 468-point 3D face landmark detection model, integrating it with AWS services including EC2, S3, and CloudWatch for scalability and performance monitoring.

Contributed to a feature for applying Snapchat-like filters, enhancing user experience. Implemented CI/CD pipelines using GitHub Actions for automated testing and deployment, ensuring efficient updates., Utilized OpenCV to develop a face detection algorithm, containerized it using Docker, and deployed it to AWS via GitHub Actions, ensuring seamless integration and performance., Leveraged the MediaPipe library to create a 468-point 3D face landmark detection model, integrating it with AWS services including EC2, S3, and CloudWatch for scalability and performance monitoring.