Binance - AI Agent Platform Engineer (Openclaw)

• Develop CLI tooling for agent operations: deployment, diagnostics, session management, skill registry, and developer workflows • CLI tooling • Own end-to-end AI agent harness engineering: lifecycle management, tool execution, context/session tuning, compaction strategies, model routing • AI agent harness engineering • 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 • data science techniques • 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) • Track the research frontier • 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 • 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 • AI Native mindset • Skill & CLI development: experience building modular, composable tools or CLI utilities for developer platforms; TypeScript and/or Python fluency • Skill & CLI development • Agent harness engineering: practical experience with OpenClaw, LangGraph, AutoGen, CrewAI, or equivalent orchestration runtimes • Agent harness engineering • LLM infrastructure: token management, model routing, context compaction, cost optimization at scale • LLM infrastructure • 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 • Data science capability • Research awareness: follows model releases, agent framework updates, and relevant literature; can quickly assess what's worth integrating and what isn't • Research awareness • Vibe coding: ships fast using AI-assisted workflows; iterative, pragmatic, high output-to-noise ratio with strong engineering fundamentals • Vibe coding • Direct experience with OpenClaw — session config, hooks, cron/heartbeat architecture, skill registry (ClawHub) • OpenClaw • Familiarity with CLI (Command Line Interface) and the agent tooling ecosystem • LiteLLM / AWS Bedrock / multi-provider proxy experience • Kubernetes/EKS: pod isolation, resource tuning, secrets management • Security engineering background: sandbox escapes, prompt injection, guardrail design