Role Description We are looking for a Cloud Site Reliability Engineer SRE to join our AI Platform team focused on building and maintaining highly available scalable and secure infrastructure for AI ML workloads This role is critical to ensure the reliability and performance of our AI services and platform components across cloud environments You will work closely with software engineers ML engineers and platform architects to design and implement robust monitoring alerting and incident response systems You ll also contribute to automation of infrastructure provisioning deployment pipelines and performance tuning of AI workloads in production Roles Responsibilities Design and implement scalable resilient cloud infrastructure to support AI ML workloads Develop and maintain observability tools including monitoring logging and alerting systems for AI platform services Automate infrastructure provisioning and deployment using Infrastructure-as-Code IaC tools Collaborate with engineering teams to ensure high availability and performance of AI services Lead incident response and root cause analysis for platform outages or performance degradation Implement security best practices and compliance controls across cloud environments Optimize resource usage and cost efficiency of AI workloads in cloud environments Participate in sprint planning and contribute to platform architecture and reliability strategy Must-Have Skills Strong experience with cloud platforms AWS GCP Azure and cloud-native services Proficiency in scripting and automation Python Bash Terraform etc Experience with containerization and orchestration Docker Kubernetes Familiarity with monitoring and logging tools Prometheus Grafana ELK Datadog Understanding of CI CD pipelines and DevOps practices Experience with incident management root cause analysis and reliability engineering Knowledge of security principles and cloud compliance frameworks Ability to learn quickly be organized and detail oriented Good-to-Have Skills Exposure to AI ML workloads and performance tuning for model inference and training Experience with MLOps tools MLflow Kubeflow Airflow Familiarity with service mesh technologies Istio Linkerd Experience with cost optimization strategies in cloud environments Knowledge of distributed systems and fault-tolerant architecture Education and Professional Certifications Bachelor s degree in computer science Engineering or related field 5-9 years of experience in cloud infrastructure DevOps or SRE roles Certifications in cloud platforms AWS Solutions Architect Azure Administrator Google Cloud SRE are a plus Soft Skills Excellent analytical and troubleshooting skills Strong verbal and written communication skills Ability to work effectively with global virtual teams High degree of initiative and self-motivation Ability to manage multiple priorities successfully Team-oriented with a focus on achieving team goals Robust presentation and public speaking skills