With 13+ years of experience building scalable enterprise distributed systems using .NET & Microsoft Azure, I work at the intersection of production architectures, Model Context Protocol (MCP), and agentic engineering workflows.
About Me
I have spent the last 13+ years designing, building, and operating enterprise software. Over my career at Dell Technologies, JustAnswer, Allscripts, Wipro PARI Robotics, and Ignite Solutions, I have worked across the full lifecycle of complex systems—from real-time robotics guidance and healthcare software modernization to cloud-native microservices on Microsoft Azure.
In recent years, I have focused on applied AI and agentic software engineering. I am passionate about how LLMs and agent frameworks elevate developer productivity and application capability. However, I remain firmly grounded in production engineering reality: systems must be deterministic, secure, maintainable, and rigorously verified.
Alongside engineering, I regularly publish architecture guides and .NET deep dives on my blog, Engineering Blog .
13+ Years of Building Production Systems
A chronological track record of my work across enterprise distributed backends, cloud-native modernization, and applied AI systems.
I architect and deliver mission-critical enterprise platforms and distributed systems on Microsoft Azure using .NET and cloud-native patterns, while driving modern agentic coding and spec-driven engineering practices across engineering teams.
I led engineering efforts for high-traffic customer-facing web platforms and automation infrastructure, combining .NET backend services, React frontends, Python automation, and early production LLM integrations.
I engineered and modernized mission-critical healthcare software applications, ensuring high reliability, patient data privacy, and seamless interoperability across clinical workflows.
I developed real-time software systems for industrial automation, high-density automated car parking, and Automated Guided Vehicles (AGVs).
I engineered client web applications, database layers, and backend services utilizing C#, ASP.NET, and modern web technologies during my early engineering career.
Architectures, Tooling & Systems I've Built
Curated laboratory of production-ready patterns, benchmarks & architecture prototypes
A centralized engineering laboratory demonstrating clean enterprise architecture patterns, benchmarks, and isolated technical prototypes across .NET 8/9, AI, Azure, and distributed systems.
Eliminates guesswork in enterprise architectures by providing concrete, battle-tested reference implementations for strongly-typed configuration, secure authentication, and decoupled abstractions.
Connecting AI coding agents to enterprise APIs, databases & telemetry via standardized MCP tools
A Model Context Protocol server and client implementation designed for enterprise software engineering. Enables AI agents to safely query database schemas, inspect API contracts, and run validated operational checks within strict permission boundaries.
Replaces fragmented, bespoke LLM tool plugins with standard Model Context Protocol servers, preventing prompt leakage and unauthorized data access while giving agents rich codebase context.
Orchestrating requirement specifications to multi-step agent coding & automated verification
An automated framework that bridges high-level requirements into formal technical specifications, executes code generation across multi-file repositories using AI agents, and enforces automated regression gates before code review.
Eliminates hallucination and drift in AI-generated code by anchoring agents to explicit interface contracts and automated Playwright / NUnit test suites.
High-throughput asynchronous messaging architecture with Outbox Pattern & CQRS
A resilient enterprise event-driven backbone engineered on Microsoft Azure Service Bus and ASP.NET Core, implementing Transactional Outbox, CQRS, and distributed tracing.
Solves dual-write inconsistencies between relational databases and message brokers, ensuring zero message loss and idempotent processing during network partitions.
How I Apply AI & Agentic Coding in Practice
The true power of AI in software development isn't just generating code snippets—it's executing coherent, multi-step engineering workflows with automated verification.
Requirements and architectural contracts (OpenAPI schemas, database entities, acceptance criteria) are established before coding begins. LLMs and agents operate within unambiguous boundaries, preventing drift and hallucination.
Rather than pasting ad-hoc snippets into chat interfaces, AI coding agents interact through standardized MCP servers. This enables agents to query live schemas, inspect repository trees, and execute validated diagnostics within strict security boundaries.
Moving far beyond simple autocomplete, coding agents execute coherent multi-step tasks across domain layers—orchestrating command handlers, DTOs, migrations, and test suites in concert.
AI output is never assumed to be correct without proof. Automated headless test suites (NUnit, Playwright), static analyzers, and security scans provide deterministic proof that generated implementations fulfill the specification.
Featured Engineering Publications
How to standardize LLM tool calling, schema discovery, and enterprise context integration using the Model Context Protocol in C# and .NET 8.
Moving beyond IConfiguration strings: implementing strongly-typed options, startup validation, and decoupled configuration in enterprise microservices.
Why senior engineering judgment and formal interface specifications are essential to harness AI coding agents without code drift.
Browse all 12 articles across Modern .NET, AI & Agents, Databases, Azure, and System Design.
My Core Technology Stack
Interested in discussing distributed systems, .NET architecture, or AI-native development?
I am always open to high-impact technical conversations, architecture advisory, or engineering collaboration.