Generative AI & Full Stack Engineer, IBM — system designeragentic AI builderfull stack engineerproblem solver
I build complex, scalable enterprise systems — then apply Generative AI and Agentic AI where they remove real engineering cost, not where they look impressive in a demo.
Not a list of technologies — a repeatable way of finding where AI is worth using at all.
I build complex software systems.
I understand the engineering problems behind them.
I identify repetitive, expensive, or difficult workflows.
I apply Generative AI and Agentic AI where it creates real value.
I design scalable solutions rather than isolated AI demos.
The result: better productivity, quality, and engineering impact.
IBM
Designing and building complex enterprise applications, customer-focused features, and AI-powered productivity systems — combining traditional software engineering with Generative AI and Agentic AI.
IBM
Hands-on experience across IBM Watson Discovery (intelligent search & information retrieval), cloud computing, and IBM Instana (application performance monitoring & observability).
Excellence Award — performance, technical contribution, commitment
Each one follows the same discipline: find an expensive manual workflow, then design an AI system around it — not the other way around.
Security Issue Resolver Agent
Transforms security vulnerability remediation from a manual, multi-hour process into an AI-assisted workflow.
Vulnerability scanner flags an issue
Resolving a CVE traditionally means identifying the vulnerability, investigating the dependency, locating the source code, determining a fix, updating dependencies, validating changes, and writing a pull request with context for reviewers — a process that can consume hours per issue.
impact
What previously took hours can be reduced to minutes — freeing developer time for feature work instead of repetitive security remediation.
Multi-Agent Development Environment
An AI development orchestration platform that turns structured specifications into implementation, tests, and review-ready pull requests.
Structured requirement is submitted
A single feature often spans backend APIs, frontend implementation, tests, and cross-repository coordination — and as applications grow, backlogs, synchronization issues, and review cycles compound.
impact
Structured specifications move toward pull requests within minutes, freeing engineers to focus on architecture and product decisions instead of repetitive implementation.
AI Unified Runtime Architect
Converts plain-English requirements into complex, ready-to-use JSON configurations for enterprise systems.
Requirement stated in plain English
Enterprise configurations can involve thousands of lines covering rules, subsets, masking policies, and mappings — historically built by searching documentation, copying old examples, and manually editing structure by hand.
impact
Over 2,000 lines of complex JSON generated in minutes, turning an hours-long documentation search into a conversational process.
Features are the visible surface. The real engineering decisions happen underneath — application boundaries, data flow, and how AI agents fit into a system that still has to be reliable at enterprise scale.
also considered
Consecutive OTAA Awards
Catalyst Award
Lines of JSON generated in minutes (AURA)
Max development effort reduction (MADE)
Database technologies
Major AI-driven engineering initiatives
KIIT — Kalinga Institute of Industrial Technology
MCA Gold Medalist
Received for three consecutive years — sustained technical contribution and impact.
For driving innovation and delivering solutions of meaningful organizational value.
IBM internship recognition for performance and technical contribution.