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Multiple amplifiers: strong Orange brand, remote option, mid-level ML role, metro location, and broad skill requirements.
Core ML and MLOps skills are transferable, but observability and telecom domain knowledge is specialized.
Explicit 5+ years, master's degree preferred, and mandatory ML/MLOps and data engineering skills.
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Own end-to-end AI/ML model lifecycle for service assurance including anomaly detection, event correlation, root cause analysis, and forecasting.
Engineer and maintain scalable data pipelines and production-grade MLOps for high-volume observability data.
Collaborate with stakeholders to translate business needs into AI use cases and quantify business impact (e.g., MTTR and alarm noise reduction).
5+ years in software/ML engineering with at least 3 years hands-on building and deploying production AI/ML models.
Strong proficiency in Python and ML libraries like Pandas, Scikit-learn, TensorFlow/PyTorch.
Experience with SQL and at least one distributed data processing framework (e.g., Spark, Flink).
Master's degree in Computer Science, Data Science, or related field, or equivalent practical experience.
Experienced in designing and industrializing AI/ML models for complex observability and network data in production environments.
Strong technical skills in MLOps best practices including model versioning, CI/CD, and monitoring for drift.
Familiarity with observability and AIOps platforms (e.g., Splunk) and interest in next-gen agentic AI/LLM-based operational tools.