Hello
— Solutions Architect bridging airline domain and enterprise AI.
About Me
An architect working at the intersection of deep airline domain knowledge and AI, with business acumen for judging what is worth solving and delivering it at enterprise grade, at scale, and governed.
“I find where the cost or risk actually lives, prove the idea fast through a POC, MLP, or MVP, weigh the risk of acting against the risk of not acting, and then shape architecture with resilience, scale, and governance designed in from the start.”
Airlines & programs delivered for
A Career Built in Aviation Tech.
Amadeus (Amadeus Software Labs India Pvt Ltd)
Leading cloud transformation and middleware modernization initiatives for airline platforms, with emphasis on distribution APIs, enterprise integration, operational architecture, and data engineering across ETL pipelines and bronze/silver layer design.
Unisys
Progressed across architecture and technical leadership roles while shaping airline PSS, offer and order management systems, AWS cloud microservices, retailing integrations, deployment patterns, and recovery strategies.
NTT DATA
Started in airline passenger systems delivery, growing quickly into client-facing engineering and strategic cutover work.
Where Deep Work Gets Done.
Application & Platform Modernization
Re-architecting legacy airline platforms into cloud-native, resilient systems — modernizing middleware, decomposing monoliths, and clearing the path for faster, safer delivery at enterprise scale.
Passenger Service Systems (PSS)
Deep, hands-on architecture across reservations, inventory, ticketing, and departure control systems (DCS) — the mission-critical systems of record that keep an airline flying.
NDC — Offer & Order Management
IATA NDC-native retailing, dynamic offer construction, ancillaries, merchandising, and end-to-end order lifecycle management — decoupling the offer from the fare and the order from the legacy ticket.
AIOps & Autonomous Observability
Agentic AIOps, including Smart Diagnose, that correlates across telemetry pipelines, distributed traces, and log-free triage with deterministic audit trails and human-reviewable reasoning.
Software Factory (Agentic SDLC)
AI and agentic software development across all phases of the SDLC — multi-agent collaboration, persona-based copilots, automated requirement synthesis, testing harnesses, and human-in-the-loop governance gates.
Homelab & Private Platform Operations
A proving ground maintained end-to-end across a fleet of self-hosted bare-metal servers — Kubernetes clusters, self-hosted LLM runners, API gateways, WAF, private DNS, and automated security pipelines.
Original Systems & Prior Art.
AI-Driven API Discovery & Dynamic Composition Engine
An intelligent recommendation and composition engine that translates natural business requirements into optimal API service execution graphs.
Discovers, ranks, and composes heterogeneous enterprise microservices based on latency, data-model compatibility, semantic contracts, and governance constraints. Published openly as defensive prior art to accelerate industry-wide API interoperability without proprietary vendor lock-in.
Constraint-Driven Electronic Document Synthesis Engine
A deterministic architecture deriving complex multi-tenant document structures directly from business constraints instead of static templates.
Generates high-precision electronic documents and layouts dynamically by evaluating structural invariants, domain rules, and strict regulatory compliance requirements, ensuring mathematical layout consistency and zero template drift.
Building in the Open.
Gives any coding agent — Claude, Copilot, Cursor — a voice on the A2A protocol.
Turns an A2A agent's skills into Model Context Protocol (MCP) tools with zero glue code.
Runs A2A agents as robust real-world apps, not raw protocol endpoints.
Tests the one thing agent frameworks forget: deterministic governance and capability boundaries.
Secure Model Context Protocol bridge for managing remote infrastructure via AI.
Technical Writing.
The Two Layers That Decide Whether Your Agent Survives
Framework choice barely matters. The harness and governance layers decide whether an AI agent survives production — with 18 runnable, tested patterns.
Your Framework Choice Matters Less Than You Think
The same governance patterns, unmodified, on LangGraph and Microsoft Agent Framework — with parity tests and the footguns found in each.
Twelve Governance Patterns On One Agent: Do They Compose?
Individual patterns are easy to believe in. Running twelve together on one agent found a real gap none of their own tests could have caught.