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Strong employer brand, broad ML lead profile and metro context increase competition, telecom niche narrows candidate pool.
Requires deep telecom domain knowledge and MLOps, reducing transferability across industries.
Explicit 5–10 years, deep telecom and MLOps technology requirements enforce strict filtering.
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Lead the design and deployment of AI/ML solutions for autonomous telecom networks enabling TM Forum Level 4+ closed-loop automation.
Build AI/ML models for anomaly detection, fault prediction, traffic optimization, QoS forecasting, and intent-based automation across RAN, Core, IPTX, and Cloud domains.
Develop and manage end-to-end MLOps lifecycle including scalable data pipelines and integration with ONAP, Kubernetes, Terraform, Ansible using CI/CD practices.
5-10+ years of work experience in AI/ML and telecom domain.
Proficient with AI/ML frameworks: TensorFlow, PyTorch, Scikit-learn, and NLP frameworks.
Experience with big data technologies: Hadoop, Spark, Kafka, Flink and MLOps platforms like MLflow or Kubeflow.
Strong telecom knowledge including RAN, 4G/5G Core, Cloud, Transport Network and network automation/orchestration.
Experienced technical lead capable of defining and executing strategic autonomous network operation roadmaps.
Demonstrated expertise in AI/ML driven network automation within telecom, comfortable with cloud-native technologies and orchestration tools.
Effective in end-to-end MLOps implementation and collaborative work in cross-functional teams dealing with advanced telecom environments.