AI/ML Software Engineer(Enterprise Networking)
Description
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:
1. Design and develop AI/ML or LLM-based modules focused on Root Cause Analysis (RCA) for network anomalies, performance degradation, and service disruptions
2. Build and optimize data pipelines to collect and process network telemetry and time-series data for ML training and inference
3. Apply machine learning algorithms to detect anomalies, classify events, and infer causal relationships within large-scale network datasets
4. Collaborate with backend, data, and networking teams to integrate RCA intelligence into production systems
5. Evaluate and improve models for accuracy, scalability, interpretability, and real-time performance
6. 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】
1. Strong programming skills in Python, with hands-on experience using ML frameworks such as scikit-learn, TensorFlow, PyTorch, or Hugging Face Transformers
2. Experience with time-series data modeling for prediction, anomaly detection, or behavioral trend analysis
3. 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
4. Knowledge of data preprocessing, feature extraction, and model evaluation
5. Strong analytical skills and ability to translate complex data into actionable insights for root cause identification and explanation
6. Ability to collaborate with cross-functional teams and deliver production-ready AI modules
【Preferred Qualifications】
1. Familiarity with networking telemetry (e.g., logs, flow data, QoS metrics) from routers, gateways, or APs — understanding how such data can feed RCA systems
2. Experience with tree-based ML algorithms such as XGBoost, LightGBM, or CatBoost.
3. Familiarity with Explainable AI (XAI) to improve model interpretability in RCA systems
4. Experience in AIOps — applying AI for proactive network monitoring and automated remediation
5. Deployment experience with AI models in cloud or edge environments