Job
Description
As a Consultant in Automation domain, you will be responsible for delivering automation use cases enabled by AI and Cloud technologies. In this role, you play a crucial part in building the next-generation autonomous networks. You will develop efficient and scalable automation solutions, you will leverage your technical expertise, problem-solving abilities, and domain knowledge to drive innovation and efficiency. You have: Bachelors degree in Computer Science, Engineering, or a related field preferred, with 8-10+ years of experience in automation or telecommunications. Understanding of telecom network architecture, including Core networks, OSS, and BSS ecosystems, along with industry frameworks like TM Forum Open APIs and eTOM. Practical experience in programming and scripting languages such as Python, Go, Java, or Bash, and automation tools like Terraform, Ansible, and Helm. Hands-on experience with CI/CD pipelines using Jenkins, GitLab CI, or ArgoCD, as well as containerization (Docker) and orchestration (Kubernetes, OpenShift). It would be nice if you also had: Exposure to agile development methodologies and cross-functional collaboration. Experience with real-time monitoring tools (Prometheus, ELK Stack, OpenTelemetry, Grafana) and AI/ML for predictive automation and network optimization is a plus. Familiarity with GitOps methodologies and automation best practices for telecom environments. Design, develop, test, and deploy automation scripts using languages such as Python, Go, Bash, or YAML. Automate the provisioning, configuration, and lifecycle management of network and cloud infrastructure. Design and maintain CI/CD pipelines using tools like Jenkins, GitLab CI/CD, ArgoCD, or Tekton. Automate continuous integration, testing, deployment, and rollback mechanisms for cloud-native services. Implement real-time monitoring, logging, and tracing using tools such as Prometheus, Grafana, ELK, and OpenTelemetry. Develop AI/ML-driven observability solutions for predictive analytics and proactive fault resolution, integrating AI/ML models to enable predictive scaling. Automate self-healing mechanisms to remediate network and application failures. Collaborate with DevOps and Network Engineers to align automation with business goals.
