Abhishek Kumar
Architect & Technical Lead – AI & Master Data Management Solutions, Red Hat
Abhishek Kumar is an experienced software engineer and technology architect with over 15 years of expertise in backend development, distributed systems, and cloud-native architectures. In his current role at Red Hat, Abhishek serves as an Architect and Technical Lead for AI and Master Data Management (MDM) solutions, where he designs and implements large-scale, intelligent data platforms that power enterprise transformation.
With a strong foundation in technologies such as Java, Python, MySQL, PostgreSQL, AWS, GCP, Apache Camel, Quarkus, and Spring Boot, Abhishek has built and optimized scalable, fault-tolerant, and high-performing systems that align with modern data governance and digital innovation needs. His experience spans the full software development lifecycle, leveraging agile methodologies and driving continuous improvement in engineering practices.
At the forefront of Red Hat's AI initiatives, Abhishek is actively building Agentic solutions using AI agents and Model Context Protocol (MCP) to automate and enhance core business operations. He combines deep technical expertise with strategic vision to integrate AI-driven automation and intelligent data processing into enterprise ecosystems.
A collaborative leader and mentor, Abhishek takes pride in guiding and empowering teams, conducting AI and MDM workshops, and sharing knowledge across the engineering community. Passionate about learning and innovation, he is dedicated to leveraging technology to build solutions that create real-world impact and accelerate digital intelligence.
Red Hat
Architect, Senior Principal Software Engineer
Session
Workshop Outline:
Phase 1: The Foundation
The "Why" & The Architecture: Brief overview of why standardized engineering (via FastAPI) is the secret sauce for moving AI from "cool demo" to "production tool."
Environment Check: Rapid-fire validation of Git, Python, VSCode, and Postman environments.
The Blueprint: Introduction to the template-mcp-server repository and its core components.
Phase 2: From Zero to Local
Cloning & Configuration: Initializing your local environment and understanding the configuration files.
The "Hello World" Run: Getting the base template running on your machine.
Walkthrough: Navigating the repository structure—where the logic lives and how the server communicates.
Phase 3: Building Your Custom Tool
The Code-Along: Step-by-step implementation of a custom "tool" within the MCP framework.
Logic Injection: Defining the tool's purpose, input parameters, and execution logic.
Live Testing: Running the server locally and verifying that the tool is discoverable and functional.
Phase 4: Integration & Orchestration
Connecting to the Agent: Integrating your local MCP server with an agent (like Cursor) or a remote orchestrator.
The Real-World Test: Watching the agent invoke your custom tool to solve a live task.
Containerisation: A quick look at the "deployment-ready" state—wrapping your server for enterprise scale.
Outcomes:
Development: Move from a blank slate to a FastAPI-based MCP server.
Extensibility: Learn exactly how to add new tools to existing templates.
Integration: Connect your server to real-world agents like Cursor.
Scalability: Leave with a containerised solution ready for production deployment.
Note: This is a high-speed lab. We prioritize doing over discussing. Ensure your Python/Git/VSCode/Postman is warmed up and ready to pull images!