Consultant - Automation

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:

• Bachelor's 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.