| Job Position | Company | Posted | Location | Salary | Tags |
|---|---|---|---|---|---|
Binance | Hong Kong, Hong Kong |
| |||
Bcbgroup | Remote | $122k - $141k | |||
Bcbgroup | Remote | $59k - $80k | |||
Zscaler | Remote | $92k - $120k | |||
| Learn job-ready web3 skills on your schedule with 1-on-1 support & get a job, or your money back. | | by Metana Bootcamp Info | |||
Zscaler | Remote | $45k - $65k | |||
Zinnia | Remote | $122k - $123k | |||
Jumpcrypto | Remote | $150k - $200k | |||
Falconx | Remote | $106k - $109k | |||
Bloxstaking | Remote | $82k - $112k | |||
Bitpanda | Remote | $90k - $96k | |||
Bitpanda | Vienna, Austria | $90k - $106k | |||
Bitpanda | Remote | $140k - $150k | |||
Bitmex | Remote | $95k - $101k | |||
Bitmex | Remote | $117k - $130k | |||
Alpaca | Remote | $88k - $108k |
AI Agent Platform Engineer
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
Is Kubernetes high demand?
Yes, Kubernetes is currently in high demand in the technology industry
Kubernetes is an open-source container orchestration platform that is widely used for deploying, scaling, and managing containerized applications
It provides a standardized way to manage and automate the deployment of containerized applications across multiple hosts and provides benefits such as reliability, scalability, and flexibility
As more and more organizations move towards containerized architectures, Kubernetes has become a critical component of their infrastructure
Kubernetes is used by companies of all sizes, from startups to large enterprises, and across various industries, including finance, healthcare, and e-commerce
According to various job market and salary surveys, Kubernetes-related skills are in high demand, and job positions related to Kubernetes are growing at a rapid pace
In fact, Kubernetes is often listed as one of the top skills that are in high demand by technology companies
Overall, Kubernetes is a highly sought-after skill in the technology industry, and it's likely to remain in high demand in the foreseeable future as more and more organizations adopt containerization and cloud-native architectures.