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Jobs/Full Stack Engineer Role/g2i - Full stack AI Engneeir - AI Acquisition
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g2i

g2i - Full stack AI Engneeir - AI Acquisition

Remote2d ago
RemoteWWFull Stack EngineerFull StackReactPostgreSQLNext.jsTypeScriptVector

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Requirements

• Strong senior full-stack experience with real depth in React, Next.js, TypeScript, Node, and PostgreSQL — not just familiarity, but the ability to make sound architectural calls under ambiguity. • Hands-on experience building production AI systems: agentic workflows, multi-model orchestration, RAG/vector databases, prompt caching, semantic memory, token tracking, and eval/testing frameworks. • A track record of owning a product area solo or near-solo — whether that's a startup, a side project that went somewhere, or being the de facto AI architect on a small team. • Strong systems thinking and adaptable problem-solving; you can reason about tradeoffs across the stack, not just within your usual lane. • Clear, direct communication — you ask good questions, explain your thinking step by step, and aren't precious about being right. • An AI-native development mindset: you're already building with AI tools, not just bolting AI features onto existing products. • Genuine excitement about async, distributed work and a small, fast-moving team. • What stands out to us • We're filtering for substance over polish. The strongest candidates tend to have shipped something real on their own — a product, an internal tool, an agent system — and can talk through the decisions they made and why, without ego or defensiveness when challenged. If you're the kind of engineer who gets energized by ambiguity rather than needing it removed for you, this role is built for people like you.

Responsibilities

• You'll own product areas end-to-end — architecture, implementation, and iteration — across our agent platform. That means building and maintaining core application infrastructure in React, Next.js, TypeScript, Node, and PostgreSQL, while also designing the AI layer that makes our agents actually work: multi-model orchestration, RAG pipelines and vector databases, semantic memory, prompt caching strategies, and token usage tracking. You'll build and maintain eval and testing harnesses to keep agent behavior reliable as models and prompts evolve, and you'll work closely with the founding team to translate fuzzy product ideas into shipped, working systems.

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