This role is part of the Automation Excellence Center (AEC) and is responsible for engineering, deploying, and operating AI/ML and GenAI capabilities that underpin agentic and closed-loop automation in telecom environments. The AI/ML Data Scientist will work hands-on across the full lifecycle—feature engineering from telecom datasets (KPIs, alarms, logs, events), model training and fine-tuning, containerized deployment, and post-deployment optimization. A key focus is embedding AI models and LLM-based components into automation frameworks, orchestration engines, and policy systems to enable event-driven decisioning and autonomous actions. The role also owns MLOps pipelines for model versioning, monitoring, drift detection, and continuous retraining, ensuring automation intelligence remains scalable, reliable, and production-grade as the organization advances toward Agentic Ops and higher Autonomous Network maturity.

• Strong hands-on expertise in Python and AI/ML frameworks (Py Torch, Tensor Flow, scikit-learn).
• Practical experience implementing GenAI and LLM-based systems (RAG pipelines, embeddings, vector databases, prompt engineering).
• Experience deploying AI components into automation platforms using Docker, Kubernetes, and cloud-native environments.
• Solid understanding of MLOps for automated systems (CI/CD for models, drift detection, observability).
• Strong understanding of telecom network operations (Core, RAN, Transport, OSS/BSS).
• Experience working with automation frameworks, orchestration engines, and workflow systems.
• Familiarity with telecom operational data (KPIs, alarms, logs, events) used in closed-loop automation.
• Experience designing agent hierarchies and multi-agent systems.
• Exposure to Autonomous Networks maturity models and intent-driven operations.
• Knowledge of observability platforms enabling intelligent automation.
• Experience in Automation CoE / Platform teams.
• 7 year+ experience

• Design, build, and fine-tune AI/ML and GenAI models that act as intelligent agents within automation workflows.
• Develop agentic AI components (decision agents, RCA agents, recommendation agents, remediation agents) that operate with minimal human intervention.
• Implement context-aware GenAI capabilities (LLMs, RAG, reasoning pipelines) to enhance automation intelligence and adaptability.
• Embed AI/ML models into closed-loop automation pipelines—from signal ingestion and insight generation to action execution.
• Integrate AI components with automation frameworks, orchestration engines, policy systems, and workflow platforms.
• Enable event-driven and intent-based automation aligned to Autonomous Network maturity (L3/L4).
• Build and operate MLOps pipelines to support model deployment, monitoring, retraining, and lifecycle governance.
• Standardize AI assets (models, prompts, agents, pipelines) for reuse and scale across multiple use cases and customers.
• Continuously tune automation models for accuracy, reliability, latency, and business impact.
• Drive AI-powered automation use cases such as anomaly detection, root cause analysis, predictive assurance, intelligent provisioning, and service experience automation.
• Work closely with network, OSS/BSS, and automation architects to ensure solutions reflect real-world telecom constraints and SLAs.
• Support multi-vendor and multi-domain automation scenarios.