Binance Accelerator Program - DevOps (Artificial Intelligence)
Team: DevOps/SRE
Location: Asia, Hong Kong, Australia, Brisbane, Australia, Melbourne, Australia, Sydney, South East Asia, Vietnam, Ho Chi Minh
Commitment: Binance Accelerator Program
Workplace Type: hybrid
Responsibilities
- Assist in the installation, configuration, and maintenance of Redis/Kafka clusters.
- Monitor Redis/Kafka performance metrics and troubleshoot issues.
- Support the development and maintenance of Kafka-based data pipelines.
- Work with development teams to integrate Kafka into applications and Participate in debugging and resolving Kafka-related problems.
- Develop and optimize Redis client libraries for applications.
- Stay informed about Redis/Kafka updates and industry best practices.
- Research and apply MCP methodologies with AI enhancements for Redis/Kafka optimization
- Help maintain documentation for Redis/Kafka procedures and AI insights for Redis/Kafka operations and troubleshooting.
Requirements
- Basic understanding of Apache Kafka and its components (brokers, topics, producers, consumers).
- Basic knowledge of Linux operating systems and Programming experience in Python/Java/Golang, experience with Redis module or Kafka development is a big plus.
- Familiarity with distributed systems and real-time data processing concepts.
- Understanding of network protocols and data serialization formats (e.g., Avro, JSON).
- Strong engineering mindset, fast learning ability, and excellent problem-solving and troubleshooting skills.
- Good communication skills and ability to collaborate effectively with team members.
- Eagerness to learn and grow in the field of data streaming and messaging systems.
- Interest in learning and applying AI/ML techniques for Redis/Kafka performance monitoring and optimization.
Key scopes
- Middleware Stability & High Availability: Assisting in maintaining high-availability Kafka/Redis clusters, despite limited production experience.
- Performance Monitoring & Alert: Filtering out irrelevant alerts while accurately identifying genuine performance bottlenecks or failures.
- Effective AI Model Integration: Ensuring AI tools accurately correlate Redis/Kafka metrics (e.g., latency, throughput, consumer lag) with real issues, avoiding false positives/negatives.
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