AI/ML Software Engineer(Enterprise Networking)

工作說明

We are looking for an AI/ML Engineer to design and implement intelligent analytics and automation features for our enterprise networking products. Your primary mission will be to build AI-driven Root Cause Analysis (RCA) systems that can automatically identify, explain, and predict network issues across routers, gateways, and wireless access points - enabling self-healing and AIOps capabilities for enterprise networks.

What You’ll Do:

• Design and develop AI/ML or LLM-based modules focused on Root Cause Analysis (RCA) for network anomalies, performance degradation, and service disruptions

• Build and optimize data pipelines to collect and process network telemetry and time-series data for ML training and inference

• Apply machine learning algorithms to detect anomalies, classify events, and infer causal relationships within large-scale network datasets

• Collaborate with backend, data, and networking teams to integrate RCA intelligence into production systems

• Evaluate and improve models for accuracy, scalability, interpretability, and real-time performance

• Stay updated on the latest advancements in ML, LLMs, Explainable AI (XAI), and AIOps, applying them to enhance enterprise network reliability and observability

需求條件

擅長工具：Python

【Essential Requirements】

• Strong programming skills in Python, with hands-on experience using ML frameworks such as scikit-learn, TensorFlow, PyTorch, or Hugging Face Transformers

• Experience with time-series data modeling for prediction, anomaly detection, or behavioral trend analysis

• Hands-on experience in either Machine Learning or Large Language Models (LLMs) - both are highly welcome:

• For ML: Feature engineering, training, and model optimization

• For LLM: Prompt design, fine-tuning, or domain-specific model adaptation

• Knowledge of data preprocessing, feature extraction, and model evaluation

• Strong analytical skills and ability to translate complex data into actionable insights for root cause identification and explanation

• Ability to collaborate with cross-functional teams and deliver production-ready AI modules

【Preferred Qualifications】

• Familiarity with networking telemetry (e.g., logs, flow data, QoS metrics) from routers, gateways, or APs — understanding how such data can feed RCA systems

• Experience with tree-based ML algorithms such as XGBoost, LightGBM, or CatBoost.

• Familiarity with Explainable AI (XAI) to improve model interpretability in RCA systems

• Experience in AIOps — applying AI for proactive network monitoring and automated remediation

• Deployment experience with AI models in cloud or edge environments