As a Machine Learning Engineer, 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 ...