ml engineer in fintech

Tickmill is an award-winning, 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. Founded in 2014, the Tickmill Group employs over 330 professionals across offices worldwide.

• 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 or 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;
• Define retraining and escalation strategies;
• 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 model retraining;
• 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 programming 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; GitHub Actions preferred;
• Strong SQL skills and experience working 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 in production systems;
• 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 retrieval-augmented generation; regulated environments and model governance frameworks; contributing to team standards, mentoring, or leading applied AI delivery.

• Attractive remuneration package based on qualifications and experience;
• Opportunities to learn and grow through the Employee Training & Development program;
• Hybrid work flexibility;
• Multiple team and group events;
• Birthday and loyalty benefits.