Graph Machine Learning Engineer (Network)

Job Title:Graph Machine Learning Engineer (Network)

Location: Remote

Duration:6 months

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.