AI Agent Platform Engineer (Openclaw)
Binance · Hong Kong +1 · 5mo ago
Binance is building one of the largest internal AI agent fleets in the industry — hundreds of sandboxed agents powering automation across trading, compliance, customer service, risk, and beyond. This role sits at the core of that platform: you'll build the infrastructure and tooling that makes every agent faster, smarter, and more impactful — directly translating into operational efficiency gains and accelerating business growth.
We're looking for a strong individual contributor who stays close to the frontier and has the instinct to turn promising ideas into working implementations.
This is a builder role. You'll own the full stack from agent skill authorship to infrastructure tuning, and you'll be the person who spots a new technique in the wild and figures out how to make it real inside our platform.
Responsibilities
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Build, publish, and maintain OpenClaw skills — modular capability units used by hundreds of agents across the org to automate repetitive work and unlock new business capabilities
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Develop CLI tooling for agent operations: deployment, diagnostics, session management, skill registry, and developer workflows
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Own end-to-end AI agent harness engineering: lifecycle management, tool execution, context/session tuning, compaction strategies, model routing
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Instrument the agent fleet with data pipelines and dashboards; apply data science techniques to understand token efficiency, failure modes, latency distribution, and business outcome correlation
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Identify bottlenecks across the platform and drive measurable improvements in agent throughput, response quality, and cost efficiency — directly supporting user growth and retention
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Track the research frontier — papers, open-source releases, community developments — and rapidly prototype integrations (new model capabilities, reasoning techniques, agentic frameworks)
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Optimize LLM infrastructure: token budgeting, multi-provider routing, cost attribution, context window management
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Harden agent sandboxes: credential isolation, prompt injection defense, guardrails
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Partner with product and business teams to translate user growth goals into reliable, scalable agent workflows
Requirements
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5+ years in software/platform engineering; 2+ year hands-on with LLM or AI agent systems in production
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AI Native mindset — you default to AI-assisted development, think natively in agent/tool/context primitives, and are allergic to doing manually what an agent could do
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Skill & CLI development: experience building modular, composable tools or CLI utilities for developer platforms; TypeScript and/or Python fluency
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Agent harness engineering: practical experience with OpenClaw, LangGraph, AutoGen, CrewAI, or equivalent orchestration runtimes
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LLM infrastructure: token management, model routing, context compaction, cost optimization at scale
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Data science capability: comfortable with log analysis, statistical profiling, SQL/Python for usage data; can translate raw telemetry into actionable insights that drive platform decisions
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Research awareness: follows model releases, agent framework updates, and relevant literature; can quickly assess what's worth integrating and what isn't
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Vibe coding: ships fast using AI-assisted workflows; iterative, pragmatic, high output-to-noise ratio with strong engineering fundamentals
Nice to have
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Direct experience with OpenClaw — session config, hooks, cron/heartbeat architecture, skill registry (ClawHub)
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Familiarity with CLI (Command Line Interface) and the agent tooling ecosystem
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LiteLLM / AWS Bedrock / multi-provider proxy experience
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Kubernetes/EKS: pod isolation, resource tuning, secrets management
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Security engineering background: sandbox escapes, prompt injection, guardrail design