Responsibilities
- Monitoring and Alerting: Creating and maintaining robust monitoring systems to proactively identify and resolve issues before they impact customers. Implementing effective alerting mechanisms to ensure timely response to critical events.
- Incident Response: Participating in on-call rotations and leading incident response efforts to minimize downtime and restore service quickly.
- Automation: Developing and implementing automation tools and scripts to streamline operations, reduce manual effort, and improve efficiency.
- Capacity Planning: Forecasting resource needs, optimizing resource utilization, and ensuring customers' infrastructure can handle increasing workloads.
- Performance Optimization: Identifying and resolving performance bottlenecks, optimizing system performance, and improving response times.
- Collaboration: Partnering with software engineers, data scientists, and other teams to ensure alignment and efficient operations.
- Customer Focus: Working closely with the AI Program Manager and Technical Account Manager to understand customer issues, provide technical support, and improve customer satisfaction.
- Continuous Improvement: Driving a culture of continuous improvement by identifying opportunities to enhance system reliability, performance, and efficiency.
Basic Qualifications
- Bachelor's degree in computer science, engineering, or a related discipline
- 5+ years of industry experience as a Site Reliability Engineer
- Experience with cloud platforms (AWS, GCP, Azure), containerization technologies (Docker, Kubernetes), observability and alerting tools (Prometheus, Grafana, ElasticSearch, Jaeger)
- Experience with scripting languages (Python, Bash)
- Working knowledge of Github, Github actions, CI/CD concepts
- Experience in ticket management, issue resolution, and troubleshooting
- Strong problem-solving and troubleshooting skills
- Excellent customer communication and interpersonal skills, fluency in verbal and written English
Preferred Qualifications
- Knowledge of AI/ML infrastructure and workloads
- Knowledge of big data technologies (Kafka, Flink)
- Knowledge of database technologies (MongoDB, PostgreSQL)