Site Reliability Engineer - JL5

Responsibilities

Roles and Responsibilities:

• Design and implement the lifecycle of services from conception to inception, including system design, build, and deployment
• Develop software solutions to enable operability of large-scale distributed systems capable of handling millions of transactions and petabytes of data
• Manage capacity and performance to help scale the infrastructure both on public and private clouds around the world
• Define and implement standards and best practices related to: System Architecture, Deployment, metrics, operational tasks
• Support services through activities such as monitoring availability, system health, and incident response
• Improve system performance, application delivery and efficiency through automation, process refinement, postmortem reviews, and in-depth configuration analysis
• Engage in Communications across all areas of the organization
• Troubleshooting and monitoring production systems to ensure the highest uptimes are maintained
• Support and improve upon existing high-availability architecture solutions as well as manage the operational activity.
• Integrate Generative AI (GenAI) and AIOps tools to automate incident detection, root cause analysis, and resolution workflows (e.g., self-healing scripts, intelligent runbooks), reducing manual toil and accelerating response times.
• Apply Prompt Engineering techniques to enhance interactions with AI-based observability and automation platforms improving accuracy and efficiency of AI responses.
• Leverage platform-specific AI capabilities (e.g., AWS Bedrock, Azure OpenAI, GCP Vertex AI) to architect intelligent SRE solutions tailored to cloud environments.
• Design, implement, and maintain AI/ML driven monitoring and alerting systems to proactively detect anomalies and predict potential failures, enabling preemptive remediation.
• Develop and train machine learning models using operational telemetry (logs, metrics, events, traces) to support predictive analytics and intelligent automation.
• Evaluate and deploy AIOps platforms (e.g., Moogsoft, Dynatrace, Splunk, BigPanda, Datadog, Elastic) to enhance observability, reduce noise, and accelerate incident resolution.
• Experience in one or more high level programming languages like Python or Ruby or GoLang and familiar with Object Oriented Programming.

Preferred skills

Technology->DevOps->DevOps Architecture Consultancy,Technology->AI-Generative AI->Artificial Intelligence - BASIC,Technology->Devops->Ansible

Education

Bachelor of Engineering