Role Overview

As a Site Reliability Engineer, you will design and implement the lifecycle of services from conception to deployment, developing software solutions to enable the operability of large-scale distributed systems capable of handling millions of transactions and petabytes of data. You will manage capacity and performance across public and private clouds, integrating Generative AI and AIOps to automate incident detection and resolution.

Responsibilities

Design and implement the lifecycle of services including system design, build, and deployment.
Develop software solutions for large-scale distributed systems handling petabytes of data.
Manage capacity and performance for infrastructure on public and private clouds.
Define standards for System Architecture, Deployment metrics, and operational tasks.
Support services through monitoring availability, system health, and incident response.
Improve performance and efficiency through automation, process refinement, and postmortem reviews.
Integrate Generative AI, GenAI, and AIOps tools for self-healing scripts and intelligent runbooks.
Apply Prompt Engineering to enhance interactions with AI-based observability platforms.
Leverage AWS Bedrock, Azure OpenAI, or GCP Vertex AI to architect intelligent SRE solutions.
Design and maintain AI/ML driven monitoring and alerting systems for predictive analytics.
Evaluate and deploy AIOps platforms such as Moogsoft, Dynatrace, Splunk, BigPanda, Datadog, or Elastic.

Requirements

Experience in high-level programming languages such as Python, Ruby, or GoLang.
Strong familiarity with Object Oriented Programming.
Ability to troubleshoot and monitor production systems to ensure high uptime.
Knowledge of Site Reliability Engineering (SRE) and DevOps principles.

Skills

Python
GoLang
AIOps
AWS Bedrock
Kubernetes