Mlops Engineer
1 week ago
About the Role:
**Key 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.
**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.
**Why Join Us**:
- Work with cutting-edge MLOps and AI deployment technologies in a fast-growing industry.
- Be part of a dynamic and innovative team focused on AI and cloud solutions.
- Gain exposure to end-to-end machine learning workflows, from data processing to model deployment.
- Opportunities for professional growth in cloud computing, automation, and AI infrastructure.
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