Found Description
As aMachine LearningEngineer , you will bridge the gap between data science prototypes and production-grade machine learning systems. You will be responsible for deploying, scaling, and monitoring machine learning models in a production environment, ensuring reliability, performance, and maintainability across enterprise use cases.
What You’ll Do and How You’ll Succeed
- Productionise ML models from Jupyter notebooks into scalable and reliable services.
- Build feature engineering pipelines and feature stores for real-time and batch inference.
- Implement model serving infrastructure, including REST APIs, batch scoring, and streaming inference.
- Design and maintain CI/CD pipelines for ML models as part of MLOps practices.
- Monitor model performance, detect drift, and trigger automated retraining where needed.
- Optimise model inference for latency and cost through techniques such as model compression, ...