ml engineer in fintech

The company is a multi-regulated broker offering access to asset classes including CFDs on Forex, Stocks, Indices, Commodities, Cryptocurrencies, and Bonds, as well as Exchange Traded Derivatives such as Futures and Options.

• Design, develop, and productionise machine learning models end-to-end, including training, validation, deployment, monitoring, and retraining;
• Lead AI use cases such as client lifetime value, churn prediction, and fraud and abuse detection;
• Build and establish MLOps practices, including model deployment pipelines, CI/CD, environment promotion, and lifecycle management;
• Implement monitoring frameworks for model performance, data drift, data quality, and business impact;
• Ensure model explainability and transparency using SHAP, feature attribution, and other interpretability methods;
• Define and enforce best practices for model governance, documentation, versioning, and auditability;
• Collaborate with Data Engineering on data pipelines, feature engineering, reproducibility, and scalable data foundations;
• Work with Product, Risk, Commercial, and other stakeholders to translate business problems into AI solutions;
• Drive continuous improvement through feedback loops, monitoring insights, and retraining strategies;
• Mentor team members and promote best practices in production AI, MLOps, and applied machine learning delivery.

• 5–8+ Years of experience building and deploying machine learning models in production environments;
• Strong Python skills and software engineering fundamentals, including testing, code quality, modular design, and maintainability;
• Strong understanding of machine learning concepts, model evaluation, feature engineering, data leakage, drift, and stability;
• Hands-on experience with large-scale data processing using Spark or PySpark;
• Experience with ML lifecycle tools such as MLflow for experiment tracking, model management, and reproducibility;
• Experience building and maintaining CI/CD pipelines for ML or data workflows;
• Strong SQL skills and experience with large, complex datasets;
• Proven ability to deliver AI/ML solutions with measurable business impact;
• Experience with model deployment, monitoring, drift detection, and retraining strategies;
• Strong communication skills with technical and non-technical stakeholders;
• Ability to work with evolving requirements, imperfect data, and delivery pressure while balancing MVP speed with production robustness;
• Nice to have: Experience in fintech, trading, or financial services; real-time or streaming ML systems; LLMs, embeddings, or RAG; regulated environments and model governance frameworks; team standards, mentoring, or leading applied AI delivery.

• Attractive remuneration package based on qualifications and experience, including 13th salary and discretionary bonuses;
• Employee Training & Development program;
• Medical insurance covering outpatient, inpatient, and dental care;
• Quarterly and semiannual team activities;
• Provident Fund Scheme;
• Birthday and loyalty benefits;
• SportBenefit collaboration.