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DevOps Engineer

Summary

Design, deploy, and maintain Kafka/Redis clusters and cloud infrastructure (AWS/Alicloud) while building AI-driven DevOps automation for alert triage, RCA, and ChatOps.

Responsibilities:



  • Handle production incidents and post-mortem analysis for system stability improvement

  • Designing, deploying, monitoring, and troubleshooting Kafka and Redis clusters in PROD environment, ensuring optimal performance and reliability

  • Work closely with development teams to ensure seamless deployment of applications or systems

  • Manage and optimize cloud infrastructure (AWS, Alicloud) for performance, cost, and reliability

  • Develop Devops platform like online load test, change management system

  • Leverage LLMs or AI frameworks (OpenAI, Dify, Agno, LangChain) to enhance automation in infrastructure operations, including intelligent alert triage, RCA (Root Cause Analysis), and chat-based operations (ChatOps)

  • Continuously explore and integrate AI-driven insights into operational processes to improve reliability, reduce noise, and empower engineering teams with intelligent decision-making.


Qualifications:



  • 5+ years of hands-on experience in Kafka and Redis operations in large-scale production environments, be able to cooperate with developers to optimize code

  • Proficient in Python / Go / Java (at least one language) and SQL programming languages

  • Hands-on experience with containerization and orchestration (Docker, Kubernetes)

  • Strong experience with CI/CD tools such as GitHub Actions, Ansible, Terraform etc

  • At least 3 years of experience with AWS cloud platform. GCP, Azure, or Ali Cloud is a plus

  • Excellent problem-solving and troubleshooting skills

  • Strong team collaboration attitude and develop partnership with other teams and business

  • Practical experience building or operating AIOps systems (anomaly detection, alert correlation, automated healing, or RCA)

  • Familiarity with LLM-based DevOps automation (e.g., building chat-based ops assistants or AI-driven observability workflows)

  • Experience using or integrating tools like Dify, Agno, or LangChain into operational workflows

See also

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