Madhan Reddy G

• Designed and implemented high-performance Kafka-based solutions for real-time data streaming, ensuring seamless integration across multiple systems.
• Architected and optimized Kafka clusters, including multi-cluster deployments for high availability, fault tolerance, and disaster recovery in enterprise environments.
• Led the integration of Kafka with various systems, including ETL pipelines, microservices, and legacy applications, improving data flow efficiency by X%.
• Developed Kafka producers/consumers, leveraging Kafka Streams and KSQL for real-time stream processing to meet business requirements and improve operational performance.
• Performed Kafka performance tuning and monitoring to ensure optimal message throughput, latency, and resource utilization in both development and production environments.
• Implemented security protocols for Kafka (e.g., SSL encryption, SASL authentication, and ACLs) to ensure data integrity and secure communication between services.
• Conducted troubleshooting and debugging for Kafka-based issues, including message delivery failures, consumer lag, and partition management, ensuring minimal downtime.
• Collaborated with cross-functional teams to gather requirements and build scalable and reliable data streaming solutions that integrated with applications, data lakes, and business intelligence systems.
• Automated Kafka administration tasks using Ansible, Terraform, and Kafka Manager, reducing operational overhead and ensuring consistency in configuration management.
• Integrated third-party systems (e.g., databases, cloud services) with Kafka using connectors and frameworks like Kafka Connect, increasing data flow efficiency by X%.
• Managed Kafka schema evolution using Schema Registry to ensure backward and forward compatibility in message formats for smooth integrations.
• Provided training and mentorship to junior engineers and team members on best practices for Kafka integration, troubleshooting, and performance tuning.
• Participated in the design and implementation of data governance policies, ensuring Kafka-based systems comply with organizational data privacy and security standards.
• Built and deployed Kafka-based microservices and integrated them with popular frameworks like Spring Boot and Apache Camel, improving system responsiveness and scalability.
• Successfully migrated legacy messaging systems to Kafka, reducing data latency by X% and improving throughput and reliability across distributed systems.
• Implemented and maintained Kafka monitoring with tools like Prometheus, Grafana, and Confluent Control Center, ensuring high system availability and early detection of issues.
• Contributed to the improvement of Kafka infrastructure, performing upgrades and patches while ensuring minimal downtime, and smooth transitions.

• Optimized the delivery of enterprise messages across the globe by upgrading Dell's messaging infrastructure with cutting-edge self-service features, automation tools, and AI algorithms.

• Brought 0 downtime for IBM MQ using RDQM and innovative active- active setup

• Played a significant role in the migration of IBM MQ to RMQ and Kafka.

• Delivered multiple lot automations for Dell software products.

• Implemented capacity buyback for Dell products, resulting in reduced license and operational costs

• Developed AI-based tool Self Heal Platform as a means to enhance performance.

• Developed a cutting-edge tool for efficient resource management called smart VM using AI.

• Recognised solution for Rabbit MQ with NFS from Dell patent committee
• 30+ shoutout awards in Dell and IBM
• 3 VP awards from Dell
• 4 Game Changer awards from Dell

• RabbitMQ Cross-Data Center Synchronization and High Availability Enhancement, RabbitMQ lacks native support for cross-Data Center clustering, resulting in latency issues during data synchronization, which hampers high availability (HA) requirements., Engineered an advanced automation utility that ensures seamless data and metadata synchronization across Data Centers, coupled with an automatic failover mechanism. This solution significantly enhanced the HA capabilities, meeting Dell's stringent availability requirements.
• AI-Driven Capacity Optimization for Middleware Platforms, Inefficient resource management led to unused objects (e.g., queues in MQ and RMQ, topics in Kafka, and accounts in MFT and HAFT) consuming unnecessary space, resources and licences., Developed an AI-based capacity buyback system that automatically detects and backs up unused objects to S3, deletes them, and notifies the appropriate clusters via email. Additionally, implemented a web-based self-service recovery tool, enabling seamless restoration of objects to the middleware, optimizing resource utilization and reducing operational costs.
• Automated File System Security Remediation, World-writable files posed significant security vulnerabilities and required constant manual intervention., Created an automated tool that identifies and remediates world-writable files while sending email notifications to relevant teams. This initiative enhanced infrastructure security and resulted in substantial operational cost savings.
• NASTEL Middleware Monitoring Decommissioning and Replacement, Dell incurred high costs from using NASTEL for middleware system monitoring., Spearheaded the decommissioning of NASTEL, developing an in-house utility for monitoring and alerting, along with a self-service portal. Successfully delivered the solution within three months, leading to significant cost savings and improved monitoring efficiency.
• Smart VM Resource Manager with Anomaly Prediction, Inefficient VM resource management led to resource wastage and capacity issues., Designed an intelligent tool that predicts VM resource anomalies and adjusts capacity accordingly. The tool forecasts resource requirements with precise timestamps, enabling proactive capacity adjustments and optimization, thereby improving resource efficiency.

Ramakrishna Reddy G

• Date of Birth: 07/18/85
• Gender: Male
• Nationality: Indian

I hereby declare that above mentioned information is true to the best of my knowledge.

First Language, C1, B1, B1

Automatically Predicting fail-over of message-oriented middleware system, 11940886

• Dell Middle ware platform
• Westpac Australian bank
• Bluedart
• Network Rail
• Ap Moller
• NHO, UK