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Machine Learning Infrastructure Engineer
3 semanas atrás
You will play a crucial role in building and maintaining scalable machine learning infrastructure. Your expertise in designing and implementing frameworks for detecting data and model drift will ensure the high reliability of ML models in production.
You will develop calibration frameworks and establish versioning practices to maintain transparency and reproducibility across the ML lifecycle. Additionally, you will design and optimize reinforcement learning (RL) orchestration pipelines for real-time execution in low-latency environments.
Responsibilities include creating automated frameworks for training, retraining, and validating ML models, enabling efficient experimentation and deployment. You will work closely with ML Scientists to ship, deploy, and maintain models, and build tools for model performance monitoring, operational analytics, and drift mitigation.
- Key Responsibilities:
- Build and maintain scalable ML infrastructure
- Design and implement frameworks for detecting data and model drift
- Develop calibration frameworks and establish versioning practices
- Design and optimize RL orchestration pipelines
- Create automated frameworks for training, retraining, and validating ML models
- Work closely with ML Scientists to ship, deploy, and maintain models
Requirements:
- At least 3 years of experience in MLOps, ML Engineering, Data Engineering, or related roles
- Proficient in using Databricks, Python, and Git workflows
- Strong understanding of model performance monitoring, drift detection, and retraining workflows