SRE Cloud Engineer

3 - 5 years

5 - 8 Lacs

Bengaluru / Bangalore Karnataka India

Posted:4 months ago| Platform: Foundit logo

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Work Mode

On-site

Job Type

Full Time

Job Description

Job Description If you are go-getter who can work with minimal guidance and supervision in emerging technologies, then you have the right opportunity here! Looking for a talented Site Reliability Engineer (SRE) to join our data engineering team for ADAS systems. This role is crucial in ensuring the reliability, performance, and scalability of our data infrastructure on cloud. You will be a part of team defining the future of software defined vehicle. 1. Work with development team to derive the application design optimal for scaling and robustness on Azure cloud 2. Implementation and maintenance of data security and privacy guidelines for cloud 3. Extensive experience building CI / CD pipeline and build in identity management into application optimally. 4.Design and build automation tooling, pipelines and APIs to simplify administration tasks across a wide variety of on-premise and cloud systems. Writing pipeline YAMLs, github action, Azure Devops pipelines, Infra as code: Terraform 5. Troubleshoot, perform root cause analysis, implement corrective/preventive actions during and after an incident. 6. Create and maintain documentation and processes for troubleshooting, maintaining, and configuring systems 7. Communication: Strong communication skills Nice to have : Expertise in Python, Kubernetes Gitops Flux, Helm - Chart & Release Gen AI tools exposure Exp: 3 to 5 years Area : Preferably from a product company or Start-up Mandatory Skills Azure Services, VM management, networking, storage, Deploy, and manage scalable and reliable cloud infrastructure. Kubernetes Containerization and orchestration using Kubernetes, Docker Monitoring and Logging Experience with monitoring tools (e.g., Prometheus, Grafana)CI / CD Pipelines Automation Bash, Power shell You get an edge! Proven experience in supporting tools or applications dealing with deep learning models, defining evaluation metrics, and suggesting innovative solutions

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