Site Reliability Engineer - Any Database Stack (ElastiCache/Redis) | AWS | Terraform | Ansible | Jenkins (4-7 Years)

Overview

Join the Webex Persistence Team within Webex Service Engineering & SRE to design, build and operate highly scalable persistence services for Webex Teams, Webex Meetings and the broader Webex Suite, across Webex Data Centers and AWS Cloud environments.

What you'll do

• Design, develop, and maintain scalable backend systems using Python Flask, SQLAlchemy, APScheduler, and cache.
• Design and manage AWS cloud infrastructure (EC2, S3, RDS, VPC, IAM).
• Implement Infrastructure as Code using Terraform and Ansible.
• Build and maintain CI/CD pipelines using Jenkins and GitHub.
• Automate infrastructure provisioning, deployments, and operational workflows.
• Monitor system performance, reliability, and availability; troubleshoot production issues.
• Support and manage streaming platforms such as Kafka.
• Build or support backend utilities/services using Python and/or Java.
• Support infrastructure for data platforms and AI/ML workloads (including GenAI/LLM systems).
• Collaborate with engineering teams to improve deployment velocity and system efficiency.

What you'll need

• 4-7 years of experience in DevOps, Cloud Engineering, SRE, or Platform Engineering roles.
• Design, develop, and maintain scalable backend systems using Python Flask, SQLAlchemy, APScheduler, and cache.
• Support and manage ElastiCache/Redis, Valkey.
• Strong hands-on experience with AWS Cloud (EC2, S3, RDS, IAM, VPC).
• Proven experience with Terraform (Infrastructure as Code).
• Strong experience with Ansible (configuration management and automation).
• Hands-on experience in building and managing CI/CD pipelines (Jenkins, GitHub).

Nice to have

• Experience with streaming systems such as Kafka.
• Hands-on experience with PostgreSQL or similar databases.
• Familiarity with OpenSearch / Elasticsearch, Cassandra (NoSQL systems).
• Proficiency in Python and/or Java for automation or backend support.
• Experience with Docker and Kubernetes.
• Exposure to AI/ML workloads, data pipelines, or GenAI systems.

Details

• Location: Bangalore, India.