Site Reliability Engineer - JL6

Key Responsibilities:

• Define and execute roles and responsibilities
• Create and deploy service lifecycles from initial design to full implementation, including system architecture, build, and deployment
• Develop software solutions that facilitate the operation of large-scale distributed systems, capable of processing millions of transactions and managing petabytes of data
• Oversee capacity and performance to ensure infrastructure scalability across global public and private cloud environments
• Establish and enforce standards and best practices concerning System Architecture, Deployment metrics, and operational tasks
• Provide support for services through activities such as monitoring availability, assessing system health, and managing incident responses
• Enhance system performance, application delivery, and overall efficiency through automation, process optimization, postmortem evaluations, and detailed configuration assessments
• Facilitate communication across all organizational areas
• Conduct troubleshooting and monitoring of production systems to maintain optimal uptime
• Enhance and manage existing high availability architecture solutions alongside operational activities
• Integrate Generative AI (GenAI) and AIOps tools to automate incident detection, root cause analysis, and resolution workflows, such as self-healing scripts and intelligent runbooks, thereby reducing manual effort and expediting response times
• Utilize Prompt Engineering methods to improve interactions with AI-driven observability and automation platforms, enhancing the accuracy and efficiency of AI-generated responses
• Utilize platform-specific AI functionalities, e.g., AWS Bedrock, Azure OpenAI, GCP Vertex AI, to design intelligent Site Reliability Engineering (SRE) solutions tailored for cloud environments
• Design, implement, and maintain AI and machine learning-driven monitoring and alerting systems to proactively identify anomalies and predict potential failures, facilitating preemptive remediation
• Develop and train machine learning models using operational telemetry logs, metrics, events, and traces to support predictive analytics and intelligent automation
• Assess and implement AIOps platforms, e.g., Moogsoft, Dynatrace, Splunk, BigPanda, Datadog, Elastic, to improve observability, minimize noise, and accelerate incident resolution
• Proficiency in one or more high-level programming languages such as Python, Ruby, or GoLang, and familiarity with Object-Oriented Programming principles

Preferred Skills:

Technology->DevOps->Site Reliability Engineering(SRE)