Description

You will architect and implement AI-driven data pipelines and autonomous agent systems for telecom network telemetry and control.

This role is on-site.

Responsibilities

• Design and build data pipelines for raw log ingestion, automated feature engineering, and dataset validation.
• Develop supervised, unsupervised, and deep reinforcement learning models for anomaly detection, clustering, and closed-loop network control.
• Implement autonomous systems using CrewAI, LangGraph, or n8n to automate reasoning and multi-step task delegation.
• Build knowledge graphs, RAG systems, and fine-tuned models for Root Cause Analysis (RCA) and diagnostic tools.
• Integrate Python prototypes with high-performance C++ modules for network-layer processing.

Required Skills

• 5+ years of experience in machine learning engineering and data pipeline architecture.
• Proficiency in Python for rapid prototyping and C++ for high-performance integration.
• Experience with CrewAI, LangGraph, or n8n for agent orchestration and guardrail implementation.
• Hands-on expertise in RAG systems, Knowledge Graphs, and model fine-tuning.
• Strong background in time-series forecasting (LSTMs, Transformers) and neural networks (GNNs, CNNs).
• Deep understanding of 5G NR/Core stack (CU/DU/OAM) and AI-native architectures.

Preferred Skills

• Experience with deep reinforcement learning for closed-loop control systems.
• Background in telecom telemetry and network evolution to 6G.

Key Skills

Education

Any Graduate