Site Reliability Engineer

* 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.

* Experience in one or more high level programming languages like Python or Ruby or GoLang and familiar with Object Oriented Programming.
* Design and implement the CI/CD/CT pipeline on one or more tool stack, like Jenkins, Bamboo, azure DevOps, and AWS Code pipeline with
* Proficiency in one or more Infrastructure as code tools (e.g. Terraform, Cloud Formation, Azure ARM etc.)
* Developing, managing monitoring tools and log analysis tools to manage operations with exposure to tools such as App Dynamics, Data Dog, Splunk, Kibana, Prometheus, Grafana Elasticsearch (and not limited to).
* Hands-On experience with AIOps Process and platforms (e.g., Dynatrace, Splunk, ServiceNow) for incident management, observability and noise reduction.
* Familiarity with Prompt Engineering, GenAI Applications, AI/ML frameworks (e.g., TensorFlow, PyTorch) or platforms (e.g., OpenAI, Vertex AI).
* Having awareness of Agentic AI solutions applicable in Operations and Support.