Site Reliability Engineer - JL5

Key Responsibilities:

• Define and oversee key responsibilities within the role
• Conceptualize and execute the entire lifecycle of services from initial design to deployment, including system architecture and implementation
• Create 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 management to enhance infrastructure scalability on both public and private cloud platforms globally
• Establish and enforce standards and best practices pertaining to System Architecture, Deployment metrics, and operational tasks
• Provide support for services through proactive activities such as monitoring system health, availability, and incident management
• Enhance system performance, application delivery, and overall efficiency through automation, process optimization, postmortem analyses, and thorough configuration assessments
• Facilitate communication across various departments within the organization
• Diagnose and monitor production systems to maintain optimal uptime
• Support and refine existing high-availability architecture solutions while managing operational functions
• Incorporate Generative AI (GenAI) and AIOps tools to streamline incident detection, root cause analysis, and resolution workflows, e
• g
• self-healing scripts, intelligent runbooks, thereby minimizing manual effort and expediting response times
• Utilize Prompt Engineering techniques to improve interactions with AI-driven observability and automation platforms, enhancing the accuracy and efficiency of AI-generated responses
• Harness platform-specific AI capabilities, e
• g
• AWS Bedrock, Azure OpenAI, GCP Vertex AI, to design intelligent SRE solutions customized for cloud environments
• Construct, implement, and sustain AI/ML-driven monitoring and alerting systems to proactively identify anomalies and foresee potential failures, facilitating preemptive remediation
• Develop and train machine learning models utilizing operational telemetry logs, metrics, events, and traces to aid in predictive analytics and intelligent automation
• Assess and deploy AIOps platforms, e
• g
• Moogsoft, Dynatrace, Splunk, BigPanda, Datadog, Elastic, to enhance observability, minimize noise, and speed up incident resolution
• Proficient in one or more high-level programming languages such as Python, Ruby, or GoLang, with a solid understanding of Object-Oriented Programming principles

Preferred Skills:

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