MLOps Engineer

Responsibilities

• Design, build, and maintain scalable ML model deployment pipelines for real-time and batch inference.
• Manage and optimize cloud-based ML infrastructure, ensuring high availability and cost efficiency.
• Implement monitoring, logging, and alerting systems for ML models in production to track performance, data drift, and anomalies.
• Automate model training, evaluation, and deployment processes using CI/CD pipelines.
• Ensure compliance with MLOps best practices, including model versioning, reproducibility, and governance.
• Collaborate with data scientists, ML engineers, and software developers to streamline the transition of models from development to production.
• Optimize model serving infrastructure using Kubernetes, Docker, and serverless technologies.
• Improve data pipelines for feature engineering, data preprocessing, and real-time data streaming.
• Research and implement tools for scalable AI development, such as Retrieval-Augmented Generation (RAG) and agent-based applications.

Qualifications

• Hands-on experience with MLOps platforms (e.g., MLflow, Kubeflow, TFX, SageMaker).
• Strong expertise in cloud services (AWS, GCP, Azure and other Clouds).
• Proficiency in containerization (Docker, Kubernetes) and infrastructure as code (Terraform, CloudFormation).
• Experience in building CI/CD pipelines for machine learning models.
• Solid programming skills in Python, Go, or Shell scripting for automation.
• Familiarity with data versioning and model monitoring tools (DVC, Evidently AI, Prometheus, Grafana).
• Understanding of feature stores and efficient data management for ML workflows.
• Strong problem-solving skills with a proactive, self-motivated attitude.
• Excellent collaboration and communication skills to work in a cross-functional team.
• Fluent in Mandarin for effective communication within a multilingual team environment.