Infrastructure Architect
Okx
Hong Kong
Who We Are
About the Opportunity
What You’ll Be Doing
- Infrastructure Architecture and Evolution: Define platform architecture, technical standards, and roadmaps, balancing scalability, reliability, security, and cost. Lead critical component design, hands-on development, and implementation across teams.
- AI Native Engineering Capabilities: Connect code, documentation, dependencies, test results, and runtime data into usable engineering context. Expose capabilities through APIs, CLIs, and SDKs for developers and agents to understand and review code, run tests, and resolve issues. Establish task evaluations, output validation, and execution permissions.
- Infrastructure for Multiple Languages: Build extensible toolchains, dependency management, testing, and delivery capabilities for ecosystems such as Go, Java, Python, C++, and Rust. Use standard protocols and SDKs to support communication across languages, interface compatibility, and consistent telemetry, reducing platform integration effort.
- Monorepo and Build Systems: Define repository structure, module boundaries, dependency policies, and code ownership to support changes across modules and independent service releases. Apply practices from systems such as Bazel and Buck2 to deliver incremental and reproducible builds, affected-test selection, remote caching, and remote execution.
- CI/CD and Quality Engineering: Build shared platforms for builds, tests, artifacts, and releases across hosts and containers. Improve configuration governance, automated quality gates, progressive delivery, health verification, and rollback to shorten delivery cycles and reduce change risk.
- Observability and Reliability: Unify metrics, logs, traces, and profiling, linking telemetry to code, configuration, dependencies, and deployments. Establish SLIs, SLOs, error budgets, and alerting practices, and feed production findings back into engineering quality improvements.
- AIOps and Automated Remediation: Combine telemetry, topology, change history, and operational knowledge to enable anomaly detection, incident investigation, root cause analysis, and controlled remediation. Use incident replay, evaluation, and feedback to reduce recovery time and repetitive operational work.
What We Look For In You
- Bachelor’s degree in Computer Science or a related field, or equivalent practical experience. At least 8 years of infrastructure or platform engineering experience, including 3 years in architecture or technical leadership.
- Deep expertise in at least one of CI/CD, build systems, observability, or AIOps, with experience designing, implementing, and operating large production systems.
- Strong knowledge of Linux, networking, distributed systems, and troubleshooting. Experience with deployment, configuration management, resource isolation, and production diagnostics in host-based and Kubernetes environments.
- Experience building infrastructure for multiple languages, with an understanding of toolchain, dependency, and runtime differences. Ability to design shared platform interfaces and extensible language integrations.
- Familiarity with open-source infrastructure practices originating at Google or Meta, such as build and dependency management with Bazel or Buck2, or cross-language communication and interface contracts with gRPC and Protocol Buffers. Experience evaluating, integrating, or optimizing relevant technologies.
- Experience with automated testing, quality gates, and release engineering, using observability and reliability measures to improve delivery workflows.
- Practical experience applying LLMs or agents to engineering workflows, including context construction, tool use, task orchestration, evaluation, and safe execution.
- Proficiency in at least one of Go, Python, Java, C++, or Rust, with strong hands-on coding and debugging skills. Ability to drive implementation through design reviews, technical guidance, and collaboration across teams.
Nice to Haves
- Experience at Google, Meta, or other leading technology companies, and a background in infrastructure, developer productivity, or AI Native engineering platforms, are preferred.
- Leading large monorepos, build platforms supporting multiple languages, remote execution platforms, or enterprise developer productivity platforms.
- Building AI-powered code review, test generation, incident diagnosis, or automated remediation systems and validating them in production.
- Operating large environments spanning hosts and containers, improving software supply chain security, or contributing to relevant open-source projects.
Perks & Benefits
- Competitive total compensation package
- L&D programs and Education subsidy for employees' growth and development
- Various team building programs and company events
- Wellness and meal allowances
- Comprehensive healthcare schemes for employees and dependants
- More that we love to tell you along the process!
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