As part of the CoE team, I have taken on various roles, worked with diverse technologies, and engaged with multiple architectural patterns. I played a key role in architecting, designing, and implementing the modernization of a legacy system. Successfully led the Industry 4.0 initiative by introducing the UNS concept and implementing a Digital Twin for an entire steel manufacturing plant.

• .NET / C#
• .Net core / API
• Data: Redis, Kusto, Azure Data Explorer, KQL, SQL
• IoT: Edge, Digital Twin, UNS,
• Event Driven: Kafka, CDC, Debezium, Event hub, serverless
• Cloud: Azure, Openshift, DevOps
• Project management: Azure DevOps, Git, Scrum Board,

Cybage is a service-based company that provides expertise in building robust and enterprise-scale applications. I was part of a small team that built and supported the training module in the in-house MIS system. I also gained experience in IoT, .NET Core API, UWP, MQTT, and POC development.

• POC, Documentation, deliverables, scrum, git, svn
• .net / C#
• IoT Edge, Docker, Containers, UWP, SignalR
• Cloud: Azure, AWS

Developed a Digital Twin solution to virtualize a steel manufacturing plant using Unified Namespace (UNS), enabling real-time monitoring and data integration. This system provided stakeholders with a comprehensive, real-time view of plant operations, eliminating the need to navigate multiple screens and data sheets. By leveraging 3D visualization, users could interact with a dynamic digital replica of the plant, improving decision-making, operational efficiency, and situational awareness.

• Use of Agile methodology resulted in faster and component wise delivery.
• Use of new technologies like Digital Twin, IoT edge, ADX and serverless
• Helped to build more reliable ML model for caster speed predition as multiple data points were available from P2-P3 level
• Never Before real-time insight on subcar tracking were now avaiable, thanks to DT, ADX and Kusto.

A key objective of the project is to optimize Purchase Order (PO) and inventory matching by leveraging advanced parameter-based algorithms. This optimization minimizes inventory waste and accelerates order fulfillment, enhancing operational efficiency and supply chain management.

• Standardized the project development process to enhance efficiency and consistency for the client.
• Improved maintainability and configurability of the 'Rule engine' by implementing the Rule Engine and Builder design patterns.
• Designed and developed an event-driven system leveraging Kafka, Change Data Capture (CDC), Redis, Microservices, and SignalR for real-time processing and scalability.
• Client is calculated to save millions of dollars due to lesser waste of inventory and better service support.