About the Company

We are an innovative fintech organisation building intelligent decision systems for digital banking, payments, and lending. Our engineering teams develop scalable data products that help financial institutions identify emerging risks, automate assessments, and make informed decisions using reliable machine learning technology.

Role Overview

We are seeking a Senior Machine Learning Engineer to lead the development and deployment of production machine learning systems supporting fraud prevention, credit scoring, and broader financial risk assessment.

This position combines applied machine learning, software engineering, and MLOps. You will take ownership of the model lifecycle, from data preparation and experimentation through deployment, monitoring, and continuous improvement. You will also help establish engineering standards that ensure risk models remain reliable, scalable, explainable, and operationally effective.

Key Responsibilities

• Design and implement production-grade machine learning systems for fraud detection, credit risk prediction, and financial decision automation.
• Develop scalable data pipelines and feature engineering frameworks for transaction, customer, and credit-related datasets.
• Build real-time and batch inference services that integrate with banking, payments, and lending applications.
• Establish reproducible training pipelines, model versioning, automated testing, and deployment workflows.
• Implement MLOps practices using tools such as MLflow, Docker, Kubernetes, and cloud-native infrastructure.
• Design monitoring frameworks for model performance, data quality, prediction latency, and model drift.
• Evaluate advanced modelling approaches, including gradient boosting, anomaly detection, and graph-based methods where appropriate.
• Work with data scientists to productionise experimental models and improve their scalability and maintainability.
• Lead technical reviews and mentor engineers on machine learning engineering, software quality, and deployment best practices.
• Collaborate with risk, compliance, security, and product teams to ensure decision systems meet operational and regulatory requirements.
• Maintain model documentation, validation evidence, and appropriate governance controls throughout the model lifecycle.

Requirements and Qualifications

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, Mathematics, or a related discipline, or equivalent professional experience.
• At least 5 years of experience in software engineering, machine learning engineering, or applied data science, including production deployment experience.
• Advanced Python skills and strong knowledge of machine learning frameworks such as scikit-learn, XGBoost, LightGBM, or PyTorch.
• Experience designing scalable training pipelines, inference services, and data processing architectures.
• Strong understanding of classification, anomaly detection, model evaluation, feature engineering, and statistical validation.
• Practical experience with MLOps, model registries, CI/CD, containerisation, and cloud infrastructure.
• Experience with SQL, distributed data processing, and event-driven systems.
• Knowledge of fraud detection, credit scoring, financial risk analytics, or payment processing.
• Understanding of model explainability, data privacy, bias assessment, and risk model governance.
• Demonstrated ability to lead technical initiatives and communicate effectively with multidisciplinary teams.

What We Offer

• Competitive salary package reflecting seniority and technical expertise.
• Flexible hybrid or remote working arrangements, depending on business needs.
• Ownership of high-impact machine learning systems used in financial decision-making.
• Modern engineering tools, cloud infrastructure, and opportunities to influence technical architecture.
• Budget and time for professional development and technical learning.
• A collaborative environment with opportunities to mentor colleagues and shape engineering practices.
• Career progression within a growing financial technology organisation.

Application Instructions

Interested candidates should submit a CV highlighting their production machine learning experience, system architecture contributions, and relevant work in fraud detection, credit scoring, or financial risk technology. Include examples of MLOps implementations or models you have successfully deployed and maintained.

Lead the engineering of intelligent financial risk systems that deliver measurable business value and dependable decisions at scale.

Are you interested in this position?

Apply by clicking on the “Apply Now” Button below!

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