flipsidecrypto - AI Agent Architect
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Requirements
• 6–10 years building production AI or data systems — not prototypes; systems that run reliably at scale under real conditions • Deep hands-on experience with multi-agent architectures: context windows, memory management, dependency graphs, and where things break in practice • Strong Python and familiarity with agent frameworks — LangChain, LlamaIndex, AutoGen, or equivalent — or a clear, documented opinion on why you built your own • Practical experience with RAG architectures, vector databases, and context window management in production settings • Experience deploying LLM-powered systems in enterprise contexts — data security, access controls, audit logging • The Stuff That's Harder to Teach • LLM failure mode literacy. You know the accuracy cliff. You know why prompting cannot fix semantic problems. You build systems that don't rely on the model getting it right every time. • Production instincts. You don't consider something done until it's been wrong three times and you've fixed it twice — and you've built the judgment to know when to ship anyway. • Strong opinions on agent design. You have a clear answer to why most agent architectures fail at enterprise scale — and you've built something that doesn't. • Systems thinking. You design for failure modes first. Happy paths are not the interesting problem. • ML research exposure — fine-tuning, RLHF, model evaluation methodology • Production AI deployments in regulated industries — financial services, insurance, healthcare • Familiarity with blockchain data infrastructure or institutional crypto
Benefits
• Competitive base salary and meaningful early-stage equity. This is a foundational technical role and we price it that way. We'll be transparent about the full picture in our first conversation. • edisyl is at the moment where the technology is proven and the enterprise problem is clear. The person who takes this role will define the architecture that production deployments run on — not inherit it. The platform is real, the customers are real, and the hard problems are still open. That's a rare place to work and a real chance to build something that matters. • Complete the online application and include responses to: 1) why this role fits where you are in your career right now, and why you are the right person for it; and 2) one example of an agent system or AI infrastructure decision you made in production — what the constraints were, what broke, and what you built to fix it. • No template. Just tell us the story.
No credit card. Takes 10 seconds.