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Mid‑level ML role with telecom specialization and known brand, moderate applicant density.
Strong telecom domain requirement reduces cross‑industry transferability, increasing sensitivity.
Explicit years plus mandatory ML, MLOps, and deep telecom expertise increases filter strictness.
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Lead design and deployment of AI/ML-driven solutions for autonomous telecom networks enabling TM Forum Level 4+ closed-loop automation.
Develop AI/ML models for anomaly detection, fault prediction, traffic optimization, QoS forecasting, and intent-based automation across RAN, Core, IPTX, and Cloud domains.
Build scalable data pipelines, manage end-to-end MLOps lifecycle, and integrate solutions with ONAP, Kubernetes, Terraform, Ansible using CI/CD practices.
5-10+ years of relevant experience in AI/ML and telecom domain.
Strong expertise in AI/ML techniques including supervised, unsupervised, reinforcement, and deep learning.
Mandatory skills: TensorFlow, PyTorch, Scikit-learn, NLP frameworks, and MLOps platforms like MLflow or Kubeflow.
Deep telecom knowledge including RAN, 4G/5G Core, Cloud, Transport Network, with experience in network automation and cloud-native technologies.
Experienced in driving roadmap and execution towards autonomous network operation implementations in telecom.
Proficient in end-to-end MLOps lifecycle management and integration with telecom orchestration and automation tools.
Comfortable working cross-functionally with stakeholders, demonstrating telecom domain expertise and technical leadership in AI/ML applied to 5G and cloud networks.