About The Company
Tata Communications Redefines Connectivity with Innovation and IntelligenceDriving the next level of intelligence powered by Cloud, Mobility, Internet of Things, Collaboration, Security, Media services and Network services, we at Tata Communications are envisaging a New World of CommunicationsThe Role: We are seeking an experienced Network Data Scientist to architect and implement the AI intelligence layer for Networking product, driving the evolution toward autonomous, context-aware, and self-operating network systems. This role blends deep networking expertise, machine learning, and agentic AI systems to power AIOps platform—enabling predictive analytics, closed-loop automation, and intelligent remediation across multi-cloud and edge environments. You will design and operationalize AI agents integrated via the Model Context Protocol (MCP) to perform intelligent reasoning and actioning across the network stack. Key Responsibilities AIOps & Autonomous Intelligence Architect and develop AI/ML-driven observability and self-healing pipelines for Unified Cloud Networking platform. Build models for predictive fault detection, root cause analysis (RCA), and anomaly correlation using large-scale telemetry and metrics data. Implement closed-loop automation to auto-remediate issues and optimize network state in real time. Collaborate with cloud engineering teams to integrate AI insights into the control plane. Agentic AI Systems Develop autonomous AI agents capable of reasoning, planning, and execution using MCP (Model Context Protocol). Enable agents to leverage real-time network context (telemetry, logs, topology, intents) to generate policy-driven actions. Implement safe execution and guardrails for explainable and reversible agent actions. Contribute to Product roadmap for intent-driven, self-operating network control planes. Data Science & Modeling Engineer and maintain data pipelines for telemetry, NetFlow/sFlow/IPFIX, SNMP, syslogs, and OpenTelemetry traces. Apply graph neural networks (GNNs), time-series forecasting, and LLM-based log analytics for event correlation and RCA. Research reinforcement learning and causal inference for adaptive network policy tuning. Build explainable ML dashboards for model decisions and anomaly visualization. MLOps & Platform Integration Deploy and manage ML workflows on Kubernetes using Kubeflow, MLflow. Automate model retraining, drift detection, and pipeline monitoring. Integrate AI outputs with network controllers and orchestrators for real-time policy enforcement. Required Skills & Qualifications Strong programming in Python (Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow, HuggingFace). Expertise in multi-cloud environments: AWS, GCP, Azure Hands-on experience with network telemetry data (NetFlow, sFlow, IPFIX, SNMP). Deep understanding of TCP/IP, BGP, OSPF, VXLAN/EVPN, and multi-cloud interconnects. Familiarity with LLMs, LangChain, LlamaIndex, or AutoGen for context-aware AI systems. Solid experience with data engineering and stream processing tools (Kafka, Spark, Flink, Airflow). Knowledge of observability stacks: Prometheus, Grafana, OpenTelemetry, and ELK. Experience in deploying and scaling ML workloads in Kubernetes-native environments. Practical understanding of the Model Context Protocol (MCP) and context injection architectures for AI agents. Experience developing or orchestrating autonomous AI agents (LangGraph, AutoGen, CrewAI, Semantic Kernel). Knowledge of agent memory, goal orchestration, and tool/action layers in distributed AI systems. Integration of LLM agents with external APIs, network orchestrators, or observability tools. Desirable Additions Experience with intent-based networking, policy graph modeling, and causal reasoning. Exposure to SDN/NFV, CNI plugins (Calico, Cilium), and networking topologies. Strong grasp of Zero Trust networking, network security analytics, or traffic classification models.
