Senior Machine Learning Engineer

What You Will Be Doing

• Develop and Deploy ML Models: Build, train, and deploy robust machine learning models focused on card authorization optimization, dynamic routing, and intelligent retries.
• Real-Time Inference Engineering: Design and maintain low-latency inference pipelines capable of scoring live payment transactions within strict millisecond SLAs.
• Feature Engineering & MLOps: Collaborate with data teams to build scalable feature stores, ensuring data quality, and automate model training/deployment pipelines (CI/CD for ML).
• Experimentation & Shadow Testing: Drive A/B testing and shadow deployment strategies to safely measure the real-world impact of your models on live traffic and revenue.
• Model Monitoring: Define and monitor key performance metrics to detect data drift, model degradation, and anomalies in production environments.

What We Are Looking For

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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.

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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.

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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.

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

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

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

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

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