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CoinMarketCap Himalayas · Posted yesterday

AI/RAG engineer

Singapore Full time

AI Engineering Machine Learning Engineering LLM Engineering RAG Development
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Indexed description

Key Responsibilities

Build the Capabilities Market (Supply Side)

  • Design and ship the official skills spanning our 14-domain taxonomy (Leverage, Assets, Portfolio, Pulse, Plays, Risk, etc.) — covering market analysis, on-chain liquidity, identity resolution, derivatives, and DeFi primitives.

  • Define publishing standards, SDK ergonomics, and the skill-signing / revenue-share contract that third-party developers will build against.

  • Own the MCP server and CLI that make skill authoring feel as easy as writing a Vercel function.

Drive Agent-Side Integrations (Demand Side)

  • Partner with teams to land CMC for Agent skills inside live agent products.

  • Implement A2A / cross-agent protocol adapters so a skill published once is discoverable and callable everywhere agents live.

  • Feed integration learnings back into the SDK — every painful integration becomes a docs page or a helper API.

Grow the Developer Community (Ecosystem & Hackathons)

  • Co-design and run CMC for Agent hackathons — tracks, bounties, reference repos, judging rubrics, and the technical support Discord during event weekends. Deep collaboration expected with BNB Hack Local Series, YZi Labs EASY Residency, and Virtuals/ACP ecosystem events.

  • Write the technical content that makes developers choose us: launch posts, cookbook recipes, video walkthroughs, and "build-with-us" threads.

  • Be the technical face at Web3 × AI events;

Engineering & Grounding

  • Improve LLM grounding on crypto-specific context (tokens, contracts, narratives) to cut hallucination and latency on trading-adjacent queries.

  • Build evaluation harnesses that measure skill accuracy, reliability, and economic honesty — not just latency and uptime.

Requirements

  • Crypto-native fluency: you can explain DEX AMMs, perp funding, bridge risk, and on-chain identity without reaching for Google. Comfortable reading Solidity even if you don't write it daily.

  • AI agent systems, hands-on: function calling, tool use, MCP, and at least one of LangGraph / OpenClaw in production — not just tutorials.

  • Developer-product instincts: you've built or heavily contributed to an SDK, CLI, or API that outside developers actually adopted — and you have opinions about why most developer docs are bad.

Originally posted on Himalayas

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