Responsibilities:

Understand the Mist UI and API architecture to be able to fix and improve the current design. Design white box test plans, automate test cases for continuous end-to-end coverage of different MIST Cloud Infrastructure Services/Components and document results. Create and maintain testbeds for networking and product testing. Responsible for infrastructure (AWS and GCP Envs) pushes and validate production before and after push and ensure no regression.
- Work closely with DevOps/SRE and QA teams.
- Analyse logs and troubleshoot issues.
- Collaborate with mentors and team members to complete coding projects within specified timelines. Automate internal processes to simplify engineering operations. Work closely with developers, addressing issues and driving solutions. Comfortable in collaboratively working with DevOps/SRE team across time zones (specifically US and EU timezones during production pushes). Develop backend software that runs on AWS and GCP public clouds to manage and monitor network devices. Utilise APIs to develop tools to maintain/monitor the production cloud systems – SQL/NoSQL/Streaming/Batch pipeline. Proactively monitoring, diagnosing, and raising oncall requests for Amazon and Google cloud environments. Analysing failures and providing support for software engineers to debug production issues across micro-services and distributed platforms. Contribute to creative coding projects that leverage AI, cloud technologies, and more. Attend related team meetings, actively participating in discussions and providing status reports.

 

Qualifications: 

BS/MS in CS/CE or similar field, with a minimum of 4 years of QA experience. Enthusiasm for learning and a proactive, hands-on attitude. Strong programming skills in languages like Python, Go, Java. Linux/Shell scripting and Git. Familiarity with networking concepts and protocols. Knowledge of L2 Switching and L3 Routing is an added advantage; WiFi Switches & Routers SDWAN Experience with databases like SQL, Redis, or Cassandra. Basic understanding of Docker/Kubernetes, micro-service architecture, AWS/GCP cloud services and deployment solutions. Basic understanding of streaming data processing, monitoring solutions. Familiarity with monitoring tools: CloudWatch or Google Monitoring, or Prometheus and Grafana, or similar. Knowledge of AI/ML & Data Science. Excellent communication skills and the ability to collaborate across teams. AWS Cloud DevOps Engineer