Gowthami Gelasam

• Deployed, configured, and managed Confluent Kafka clusters on Google Cloud Platform, OpenShift Container Platform, and AWS.
• Configured and administered Confluent Kafka components including Schema Registry, Replicator, Kafka Connect, Control Center.
• Created, configured, and maintained Kafka topics and partitions to optimize performance and support business requirements.
• Implemented Confluent RBAC for efficient Active Directory Authentication/Authorization.
• Hands-on experience with Kafka MQ connectors.
• Configured Kafka Replicator for high availability across multiple data centers.
• Configured and maintained Schema Registry, ensuring schema validation and management for Avro, JSON and Protobuf formats.
• Assisted in troubleshooting Kafka consumer and producer issues, providing support for real-time and batch data processing pipelines.
• Experience with Kafka cluster administration in Production and working with CI/CD tools: GIT, Helm Charts, Bamboo and Artifactory. Configuring, monitoring and Kafka cluster with Confluent Control Center.
• Improved cluster performance through regular tuning of Kafka parameters (e.g., retention policies, replication factors, log segment sizes).
• Experience in requirement gathering, analysis, and designing mapping documents. Closely worked with the client on requirements and deliverables.
• Accountable for delivering integration design for critical banking applications, as per the industry standards and business requirements.
• Worked on creating service design documents, production implementation plans, and checklists.
• Following Agile/Scrum methodology and playing the role of Scrum Master to ensure the squad won't have any blockers and run the daily stand-up.
• For the Openbanking project, I utilized Node.js and JavaScript to create and manage microservices that optimize application scalability and efficiency.
• I played a key role in developing and integrating MOD and ecom APIs, ensuring seamless data interactions. By employing APIMesh and IBM DataPower, I enhanced API security and governance.
• My expertise includes implementing CI/CD pipelines using Codefresh, Bitbucket, and Bamboo, as well as streamlining deployment processes with uDeploy, and deploying applications on Google Cloud Platform (GCP).

• Utilized Carla platform to optimize validation methods ensuring safer autonomous navigation.
• The aim of this project is to advance research by integrating a system into a realistic driving simulator that can detect, recognize, and track road objects, making decisions that assist the user in making the driving process safer and more convenient.

Roles and responsibilities:

• Experienced working in the Python language.
• Worked with large datasets, preferably with an understanding of Hadoop, HBase, Docker, and Apache Spark.
• We used the deep-learning network YOLO algorithm, combined with data, to detect and classify the objects, and estimate the position of objects around the car.
• The algorithm has been developed and tested using the Camera dataset collected by the ROS bag.
• The performances are evaluated in controlled scenarios of increasing difficulty, and examined via metrics provided by CARLA.