AI Architecture
Agentic systems, context engineering, model and tool integration, guardrails, evaluation and production topology.
AI Architect & Forward Deployed Engineer
From complex problems to production-ready AI systems.
I bridge business context, software architecture and agentic engineering to design, deploy and scale AI capabilities inside complex enterprises.
Architecture as a delivery system — not a diagram.01 / 05
Positioning
The value of AI is not the demo. It is the ability to operate reliably inside the real constraints of an enterprise.
My work sits at the intersection of business ambiguity, architectural decisions and hands-on engineering. I help teams turn AI potential into production capabilities that are governed, observable and useful.
That means working forward-deployed: close to stakeholders, developers, data, platforms and operational constraints — while preserving a system-level view.
Areas of practice
Strategy, architecture and execution in the same operating model.
Forward Deployed Engineering
Working close to complex business problems to prototype, integrate, validate and deploy solutions in real environments.
Engineering Enablement
Platforms, standards, training and AI-assisted development practices that multiply engineering teams.
Selected impact
Complex systems. Practical decisions. Measurable outcomes.
Selected themes from enterprise work. Client details are intentionally abstracted.
Modernizing digital banking architecture without interrupting the business.
Evolving channel platforms, BFFs and integration layers to reduce duplicated processing, lower mainframe consumption and create a safer path for continuous modernization.
Architecture decisions connected to operational and financial impact.
Turning coding agents into an engineering capability — not isolated experimentation.
Creating structured workflows, specifications, context patterns, governance and practical learning paths so teams can use AI with more consistency, control and leverage.
Human judgment amplified by repeatable AI-native engineering practices.
Building reusable foundations for secure and observable AI delivery.
Designing shared services and architectural patterns for models, context, tools, resilience, billing, observability and governance — reducing friction between experimentation and production.
Reusable capabilities that increase speed without removing control.
Selected open source
Small tools for recurring problems in AI-native engineering.
Projects focused on context, informed control and better collaboration between humans and coding agents.
Context Wiki / Docs Hub
AI-native knowledge infrastructure for ingesting agent-generated documentation and making it usable by people, coding agents, MCP integrations and project context.
Devin Memory
A local memory and context layer for Devin CLI that warns before the context window fills, saves session snapshots and restores work after clearing.
WTF Approve
An agent-agnostic skill that translates execution requests into plain-language intent and risk before users approve commands in Claude Code, Codex or Gemini CLI.
Operating model
A forward-deployed loop from ambiguity to scale.
Frame the real problem
Understand the business outcome, constraints, users, systems and operational reality.
Design the system
Shape boundaries, context, models, tools, controls and integration points.
Prove it in production
Build, integrate, measure and learn with the people who will operate the solution.
Create organizational leverage
Turn what worked into platforms, standards, patterns and reusable capability.
About
Built from engineering depth. Expanded through systems thinking.
I am an AI Architect and Forward Deployed Engineer with more than 17 years in technology, currently working at NTT DATA and collaborating with complex enterprise environments.
My path moved from hands-on software development to engineering leadership, enterprise architecture and AI-enabled delivery. I remain close to implementation because architecture becomes valuable only when it improves how systems are built and operated.
Today, my focus includes agentic engineering, AI in the software development lifecycle, context infrastructure, platform architecture, modernization and technical enablement.
AI Architect & Forward Deployed Engineer
Enterprise AI architecture, AI-assisted engineering and production delivery.
Chief Architect & Engineering Leader
Architecture strategy, modernization, platform evolution and engineering enablement.
Software Engineer
Java, distributed systems, cloud, web platforms and performance engineering.