MLOps/AIOps SRE - Software Engineer(Enterprise Networking)

工作說明

We are looking for an MLOps/AIOps-focused Site Reliability Engineer (SRE) to build and maintain reliable, automated, and scalable pipelines and infrastructure for our AI/ML systems supporting enterprise networking products. This role bridges DevOps, machine learning lifecycle management, and cloud backend reliability to ensure our AI-driven services operate efficiently and accurately.

What You’ll Do:

• Build, maintain, and automate end-to-end ML pipelines including data ingestion, feature processing, model training, validation, deployment, and monitoring.

• Implement model serving and scalable inference systems on Kubernetes or similar platforms.

• Develop model performance monitoring, drift detection, and alerting mechanisms.

• Collaborate closely with AI/ML engineers to optimize training workflows, model packaging, and reproducibility.

• Implement AIOps workflows, integrating observability data, anomaly detection, ML-based insights, and automated remediation.

• Automate management of ML artifacts, datasets, and feature stores.

需求條件

擅長工具：Python

【Essential Requirements】

• Strong scripting skills (e.g., Python, Bash).

• Hands-on experience with ML workflow orchestration, such as Kubeflow, MLflow, Airflow, or similar.

• Proficiency with Git and modern CI/CD systems (e.g., Jenkins, GitHub Actions, GitLab CI).

• Solid experience with Docker and Kubernetes in production environments.

• Experience with monitoring/logging systems (Prometheus, Grafana, ELK).

• Familiarity with Infrastructure-as-Code using Terraform or equivalent.

• Understanding of ML model lifecycle, versioning, packaging, and deployment.

【Preferred Qualifications】

• Deep knowledge of MLOps frameworks (e.g., MLflow, Kubeflow Pipelines, Feast, BentoML, Seldon).

• Experience with Golang, especially for performance tools, operators, or controllers.

• Experience implementing AIOps systems, such as anomaly detection from metrics/logs/traces, automated remediation, AI-driven incident prediction.

• Experience integrating ML with network analytics, telemetry pipelines, and edge/cloud inference.

• Familiarity with Cilium, Falco, or other cloud-native security and observability tools.

• Experience with GPU-accelerated workloads, distributed training frameworks, or data engineering pipelines.