Tekgence Inc is seeking a Senior AI / Machine Learning Engineer to embed within a network engineering organization. This role will focus on leveraging Graph Machine Learning and data-driven approaches to model network environments, generate actionable insights, and improve overall network performance, reliability, and efficiency.
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
Requirements:
- 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
- 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