Senior AI/ML Engineer Service Assurance

Date de publication : Apr 27, 2026, 12:00AM Dure du contrat : Orange Business is here! About usJoin us at Orange Business! We are a network and digital integrator that understands the entire value chain of the digital world, freeing our customers to focus on the strategic initiatives that shape their business. Every day, you will collaborate with a team dedicated to providing consistent, sustainable global solutions, no matter where our customers operate. With over 30,000 employees across Asia, the Americas, Africa, and Europe, we offer a dynamic environment to develop and perfect your skills in a field filled with exciting challenges and opportunities. About the role As a Senior AI/ML Engineer, you will be at the heart of transforming our Service Assurance capabilities. Your mission will include (and not be limited to) leverage cutting-edge AI to predict and prevent service disruptions, automate root cause analysis, and ensure our network runs with unparalleled reliability. You will be responsible for exploring service and network data, designing and implementing AI/ML and agentic capabilities on top of Splunk and Ciena Blue Planet UAA, and industrializing these solutions into production-grade features. This role combines hands-on data exploration, model development, and solid software engineering to deliver advanced analytics for anomaly detection, event correlation, root cause analysis, and forecasting. About you Key Responsibilities 1-Core Model Development & Innovation Design and Implement AI/ML Solutions:Own the end-to-end lifecycle of AI/ML models for service assurance, covering anomaly detection, event correlation, root cause analysis, and capacity/traffic forecasting. This includes data discovery, feature engineering, model training, evaluation, and deployment.Prototype and Industrialize Agentic AI:Explore and build next-generation operational tools, such as LLM-based copilots for triage, agents that reason over telemetry and topology, and natural-language interfaces for observability data.Stay at the Forefront of AIOps:Continuously research advances in ML for observability, proactively prototyping and industrializing innovative ideas that enhance our service-assurance capabilities.2-Engineering & MLOps Excellence Build Scalable Data Pipelines:Engineer and maintain robust pipelines to ingest, transform, and prepare high-volume observability data (metrics, logs, alarms, tickets, topology) for AI/ML workloads.Implement MLOps Best Practices:Ensure our solutions are production-grade by implementing model versioning, automated testing, CI/CD for ML, and continuous monitoring for data and model drift.Deliver Reusable Capabilities:Expose AI/ML and agentic features as well-documented, reusable components that integrate seamlessly into dashboards, alerts, and operational workflows.3-Strategy & Collaboration Translate Business Needs into AI Use Cases:Collaborate closely with the AI lead and service-assurance stakeholders to understand operational challenges and define high-impact AI/ML projects.Measure and Communicate Impact:Quantify the business value of your solutions (e.g., MTTR reduction, alarm noise reduction) and effectively communicate results and trade-offs to technical and non-technical partners. Required Skills and Experience 1- Core Technical Skills Experience: 5+ years in software and/or ML engineering, with at least 3 years of hands-on experience building and deploying production AI/ML models.Programming: Strong proficiency in Python and its standard data science/ML libraries (e.g., Pandas, Scikit-learn, TensorFlow/PyTorch). Ability to write clean, testable, production-ready code.Data Engineering: Solid ExperiencewithSQL and at leastonedistributed dataprocessing framework(e.g., Spark, Flink) tohandlelarge-scale datasets.MLOps:Hands-on experience with MLOps tooling and methodologies (e.g., MLflow for versioning, CI/CD for model deployment, monitoring for drift).2-AI/ML Expertise Machine Learning Foundations: Solid understanding of ML algorithms (especially for anomaly detection, classification, and time-series forecasting) and the ability to choose the right tool for the job.Feature Engineering: Proven ability to explore diverse data sources (logs, metrics, events, topology), assess data quality, and derive meaningful features.Agentic AI Concepts: A strong interest in and understanding of agentic AI concepts (LLM-based agents, tool-calling, reasoning) and a drive to apply them to real-world operational problems.Observability/AIOps Platforms: Familiarity with platforms like Splunk, ELK, or Prometheus for analyzing logs, metrics, and alerts.3-Professional & Strategic Skills Problem Solving & Communication: Strong analytical skills to translate complex operational problems into concrete technical solutions. Ability to communicate effectively with both technical and non-technical stakeholders.Education: Masters degree in Computer Science, Data Science, or a related field Da