Senior Machine Learning Engineer

Classical & Deep Learning Mastery: Deep practical expertise in designing and   tuning high-performance classical ML models (e.g. XGBoost, LightGBM, Random Forests) as well as experience with deep learning. 

Ability to rigorously evaluate the trade-offs between  model complexity and inference latency as well as experience beyond standard accuracy metrics utilising calibration curves, cost-sensitive learning, and precision-recall trade-offs.

 Software Engineering & Python: Software engineering best practices, Python mastery and experience with the standard ML/Data libraries (Scikit-Learn, Pandas, Numpy) with a strong focus on writing scalable, production-ready code.  

Real-Time Systems: Proven ability to build, deploy, and optimize ML models that operate under strict latency and high-throughput constraints.  

MLOps Proficiency: Experience taking models from notebooks to production environments using tools like MLflow, Docker, Kubernetes, and CI/CD pipelines.  

Strong SQL Proficiency: Ability to write complex queries and wrangle large-scale transactional datasets for feature extraction.  

Payments Domain Knowledge (Nice to Have): Understanding of the card payment lifecycle, authorization processes, issuer behavior, 3D Secure, and network rules (Visa, Mastercard).  

Cloud Infrastructure: Proven experience deploying and managing ML systems on AWS or similar, including expertise in infrastructure as code.