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š» Fullstack AI Engineer Available for Hire
Looking for powerful, production-ready applications that seamlessly leverage the latest AI models? I specialize in:
ā
Multi-Agent Orchestration & Autonomous Workflows (Gemini, OpenAI)
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High-Performance Fullstack Web Apps (Next.js 14, React, Vue, Laravel,Python,Go)
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End-to-End RAG Pipelines & Semantic Vector Search (Supabase, pgvector)
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Scalable DB & Backend Infrastructure (PostgreSQL, AWS, Railway, Docker)
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Enterprise Workflow Automation (n8n, webhook architectures)
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Advanced Media Processing Pipelines (Self-hosted FFmpeg & BullMQ queues)
My focus is creating clean, resilient, and highly optimized digital systems that actually work in production and automate complex business logic.
š© Let's collaborate to architect your next intelligent platform and bring your product ideas to life with clean code and cutting-edge AI.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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But speed alone does not make a product good.
The real value is still in knowing what to build, how to structure it, what can break, how users will behave, and how to turn a rough idea into something reliable.
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One of the biggest misconceptions in AI:
"If we switch to a better model, our results will improve."
Sometimes.
But often, the real problem isn't the model.
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That's why the highest-performing AI products focus on:
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Better retrieval
ā
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Better system design
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In many projects, improving the context layer delivers a bigger impact than changing the model itself.
The future of AI isn't just model engineering.
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And that's where great AI products are built. š
What's been your experienceāmodel upgrades or better context? š
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