DevOps Engineer
Summary
Maintains Kafka/Redis clusters, cloud infrastructure, and CI/CD pipelines; builds AI-driven DevOps tools for alert triage, RCA, and ChatOps in AWS/Alicloud environments.
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