Hybrid AI Agent Platform Engineer

at Binance

• Job title: AI Agent Platform Engineer
• Job type: Full Time
• Experience level: Senior, Lead
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Python, SQL, TypeScript
• Location requirements: Hybrid, Hong Kong

About the role

• AI Agent Platform Engineer creating and maintaining infrastructure for AI agents at Binance. Focused on automation across trading, compliance, and customer service.

Responsibilities

• 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
• Develop CLI tooling for agent operations: deployment, diagnostics, session management, skill registry, and developer workflows
• Own end-to-end AI agent harness engineering: lifecycle management, tool execution, context/session tuning, compaction strategies, model routing
• 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
• Identify bottlenecks across the platform and drive measurable improvements in agent throughput, response quality, and cost efficiency — directly supporting user growth and retention
• Track the research frontier — papers, open-source releases, community developments — and rapidly prototype integrations (new model capabilities, reasoning techniques, agentic frameworks)
• Optimize LLM infrastructure: token budgeting, multi-provider routing, cost attribution, context window management
• Harden agent sandboxes: credential isolation, prompt injection defense, guardrails
• Partner with product and business teams to translate user growth goals into reliable, scalable agent workflows

Requirements

• 5+ years in software/platform engineering; 2+ year hands-on with LLM or AI agent systems in production
• 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
• Skill & CLI development: experience building modular, composable tools or CLI utilities for developer platforms; TypeScript and/or Python fluency
• Agent harness engineering: practical experience with OpenClaw, LangGraph, AutoGen, CrewAI, or equivalent orchestration runtimes
• LLM infrastructure: token management, model routing, context compaction, cost optimization at scale
• 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
• Research awareness: follows model releases, agent framework updates, and relevant literature; can quickly assess what's worth integrating and what isn't
• Vibe coding: ships fast using AI-assisted workflows; iterative, pragmatic, high output-to-noise ratio with strong engineering fundamentals

Benefits

• Competitive salary and company benefits
• Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team)