Founder & CEO at Magnence — an AI & Software Development Company building intelligent products from architecture through production. I work across the full stack of modern AI systems: generative AI and LLM applications, RAG pipelines, agentic workflows, and the cloud/DevOps/LLMOps layer that keeps them reliable once they ship.
I care less about knowing every framework and more about shipping systems that hold up in production — observable, secure, and cost-conscious, not just demoable.
Generative AI apps · LLM systems · RAG pipelines · AI agents & agentic workflows · multi-model orchestration · embeddings & vector search · AI-powered SaaS
Backend systems & REST APIs · full-stack apps · microservices · database architecture · auth & authorization · API integrations · developer tooling
Containerized apps · cloud-native architecture · CI/CD pipelines · production deployment · infra automation · monitoring & observability
Model integration & prompt pipelines · RAG evaluation · guardrails & confidence thresholds · observability · multi-provider LLM architecture
AI-powered developer tooling that analyzes GitHub repositories to understand architecture, project structure, data flow, workflows, documentation, dependencies, and development patterns.
AI Code Intelligence
Repository Analysis Architecture
Privacy-first, self-hosted job search orchestrator — multi-platform discovery and tracking without handing data to a third party.
AI Agents Automation
Self-Hosted FastAPI PostgreSQL
Edge-AI adaptive traffic signal system using YOLOv8 and Webster's Algorithm for real-time optimization.
Computer Vision YOLOv8
Edge AI Real-Time Systems
Building good AI systems takes more than connecting an application to an LLM.
A production-grade system needs a complete engineering lifecycle:
DISCOVER
Define the problem, users, constraints, requirements, and success criteria.
ARCHITECT
Design the system architecture, services, APIs, data flows, and infrastructure.
DATA
Build reliable data pipelines, knowledge sources, schemas, embeddings, and storage.
INTELLIGENCE
Integrate models, prompts, agents, RAG, retrieval, tools, and orchestration.
VALIDATE
Apply guardrails, evaluation, confidence thresholds, testing, and human oversight.
SHIP
Deploy, observe, optimize, secure, and continuously improve the production system.
01 Discover
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02 Architect
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03 Data & Knowledge
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04 AI / LLM Layer
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05 RAG & Retrieval
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06 Guardrails & Validation
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07 APIs & Integrations
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08 Deployment
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09 Observability
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10 Continuous Improvement