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.