AI Engineer

AI-Native Full Stack Engineer for Hire

Abin PM is an AI-native full stack developer based in Kochi, India — among the first generation of senior engineers to adopt AI-assisted development tools in enterprise production. Since 2024, he has used Cursor AI, GitHub Copilot, and Claude as core instruments in his daily workflow at IBM India, delivering React and Node.js systems for National Grid faster than traditional development cycles allow. He doesn't just use AI — he stabilizes AI-generated code for Fortune 500 production standards.

What AI-native means in practice

Feature development: weeks
Days, using AI scaffolding + senior review
Debugging: manual trace
AI-assisted root cause in minutes
Code review: manual pass
AI-assisted analysis + human judgment
Architecture: blank page
AI scaffold refined with enterprise patterns

The rare skill — GenAI stabilization

At IBM, I was assigned to the National Grid MDS Consolidation EPO Tracking project. The initial codebase was generated by AI tools — and it was a mess of subtle bugs, architectural misalignments, and enterprise anti-patterns. My job was to take that GenAI baseline, review every module, correct discrepancies, enforce React and Node.js enterprise standards, optimize the .NET Core API, and ship a production-ready application that met National Grid's reliability requirements.

That process — critically reviewing AI output, applying senior engineering judgment, hardening to enterprise standards — is what I call GenAI stabilization. It is a skill that requires deep full stack experience AND AI fluency. Most engineers have one or the other. I have both.

AI tools I use

Cursor AI
Primary IDE

Daily driver at IBM for AI-assisted coding, refactoring, and accelerated development across enterprise React and Node.js codebases

GitHub Copilot
Code Completion

Inline AI suggestions integrated into every phase of development — from feature scaffolding to unit test generation

Claude
Architecture & Review

Complex problem solving, code review strategy, and GenAI component stabilization for enterprise production requirements

OpenAI Codex
LLM Integration

API integration and AI-powered feature development within client products

v0 by Vercel
UI Generation

Rapid UI scaffolding to accelerate frontend delivery timelines significantly

Who this is for

🚀

Startups needing speed

Ship faster without burning budget on a large team.

🏢

Enterprises exploring AI

Get a proven AI-native engineer who understands compliance and scale.

🤖

Companies building LLM features

Integrate AI APIs, chatbots, and semantic search into your product.

🛡️

Teams needing GenAI hardened

Review, refactor, and productionize AI-generated codebases.

AI engineering FAQ

Is AI-generated code safe for production?

Not by default. AI-generated code requires senior review, architectural alignment, edge case handling, and testing before it is production-ready. This is exactly what I do — I take AI-generated baselines and harden them to enterprise standards.

Can you build LLM features into my existing app?

Yes. I have hands-on experience integrating LLM APIs (OpenAI, Claude) into production applications — chat interfaces, semantic search, AI-powered workflows, and more.

How do you ensure quality when using AI tools?

AI accelerates drafting and iteration. I apply senior engineering judgment to validate correctness, enforce coding standards, write tests, and ensure the output is maintainable and production-reliable. AI is the accelerator; I am the quality gate.

What is GenAI stabilization?

GenAI stabilization is the process of taking AI-generated code and correcting its discrepancies, aligning it with business logic, enforcing architecture standards, and ensuring it meets production reliability thresholds. I did this at IBM for the National Grid MDS project.

Ready to work with an AI-native engineer who can build fast AND stabilize AI-generated code?