Graph Machine Learning Engineer

Job Title:Graph Machine Learning Engineer (Network) Location: Remote Duration: 6Months contract Note: This is not a traditional network engineering or cybersecurity role. The focus is on advanced analytics, machine learning, and system-level intelligence, not configuration management or vulnerability remediation. Position Overview: We are seeking a Senior AI / Machine Learning Engineer to embed within a network engineering organization and apply advanced AI techniques to large-scale enterprise network systems. This role will focus on leveraging Graph Machine Learning (Graph ML) and data-driven approaches to model network environments, generate actionable insights, and improve overall network performance, reliability, and efficiency. Key Responsibilities: • Model complex enterprise network environments (devices, connections, traffic flows) as graph-based systems • Develop and deploy machine learning models to support: • Network anomaly detection (operational and performance-related) • Traffic analysis and forecasting • Root cause analysis across distributed systems • Apply AI/ML techniques to optimize network performance, including: • Traffic routing and load balancing • Capacity planning and demand prediction • Build pipelines to ingest, process, and analyze network telemetry data (e.g., logs, flows, metrics) • Partner closely with network engineering teams to translate business and operational challenges into AI-driven solutions • Deliver insights and recommendations that improve observability, resiliency, and operational efficiency Required Qualifications • Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field • 5+ years of experience in AI/ML, Data Science, or a related technical role • Strong proficiency in Python and experience with ML frameworks (e.g., PyTorch, TensorFlow) • Experience working with graph-based data structures or Graph Machine Learning concepts • Understanding of enterprise network fundamentals (topology, routing, switching, protocols such as TCP/IP) • Experience building and deploying machine learning models in production environments Preferred Qualifications • Experience with Graph ML libraries (e.g., PyTorch Geometric, DGL) • Familiarity with network telemetry data sources (NetFlow, SNMP, logs, etc.) • Experience with time-series analysis and anomaly detection techniques • Exposure to large-scale distributed systems or telecom/network environments • Knowledge of optimization algorithms or performance engineering techniques Core Competencies • Strong analytical and problem-solving skills • Ability to work cross-functionally with engineering and operations teams • Experience translating complex technical concepts into actionable insights • Self-driven with the ability to operate in ambiguous, evolving environments Role Scope This position focuses on applying AI to network operations and optimization, enabling a shift from reactive management to proactive, intelligent network systems.