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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. |
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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 |
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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 |
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AI-powered developer tooling that analyzes GitHub repositories to understand architecture, project structure, data flow, workflows, documentation, dependencies, and development patterns.
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Privacy-first, self-hosted job search orchestrator — multi-platform discovery and tracking without handing data to a third party.
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Edge-AI adaptive traffic signal system using YOLOv8 and Webster's Algorithm for real-time optimization.
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Building good AI systems takes more than connecting an application to an LLM.
A production-grade system needs a complete engineering lifecycle:
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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