machine learning engineer

Tickmill is an award-winning, multi-regulated broker offering access to a broad range of asset classes, including CFDs on Forex, Stocks, Indices, Commodities, Cryptocurrencies, and Bonds, as well as Exchange Traded Derivatives like Futures and Options.

• Design, develop, and productionise machine learning models end-to-end (training, validation, deployment, monitoring, retraining), ensuring reliability in real-world environments;
• Lead the development of AI use cases such as client lifetime value (CLV), churn prediction, and fraud/abuse detection, with clear alignment to business outcomes and measurable impact;
• Build and establish robust MLOps practices, including model deployment pipelines, CI/CD, environment promotion (dev/stage/prod), and lifecycle management;
• Implement model monitoring frameworks to track performance, data drift, data quality, and business impact, with clear retraining and escalation strategies;
• Ensure model explainability and transparency using techniques such as SHAP, feature attribution, and other interpretability methods appropriate for business-critical and regulated contexts;
• Define and enforce best practices around model governance, documentation, versioning, and auditability, proportional to model risk and business impact;
• Collaborate closely with Data Engineering to ensure high-quality data pipelines, feature engineering, reproducibility, and scalable data foundations;
• Work cross-functionally with Product, Risk, Commercial, and other stakeholders to translate business problems into pragmatic AI solutions, balancing speed and robustness;
• Drive continuous improvement through feedback loops, monitoring insights, and model retraining strategies, rather than one-off model delivery;
• Mentor team members and promote best practices in production AI, MLOps, and applied machine learning delivery.

• Have 5–8+ years of experience building and deploying machine learning models in production environments (not just experimentation);
• Possess strong Python programming skills and solid software engineering fundamentals (testing, code quality, modular design, maintainability);
• Demonstrate strong understanding of machine learning concepts, model evaluation, feature engineering, and practical considerations in production systems (e.g. data leakage, drift, stability);
• Bring hands-on experience with large-scale data processing (Spark / PySpark);
• Have experience with ML lifecycle tools (MLflow or similar) for experiment tracking, model management, and reproducibility;
• Have experience building and maintaining CI/CD pipelines (GitHub Actions preferred) for ML or data workflows;
• Show strong SQL skills and experience working with large, complex datasets in real-world environments;
• Prove ability to deliver AI/ML solutions with measurable business impact, not just model performance improvements;
• Have experience working with model deployment, monitoring, drift detection, and retraining strategies in production systems;
• Exhibit strong communication skills with the ability to work effectively with both technical and non-technical stakeholders, translating trade-offs clearly;
• Have the ability to operate effectively in environments with evolving requirements, imperfect data, and delivery pressure, balancing MVP speed with production robustness;
• Nice to have: experience in fintech, trading, or financial services environments, particularly where models influence business-critical decisions, experience with real-time or streaming ML systems, familiarity with modern AI approaches such as LLMs, embeddings, or retrieval-augmented generation (RAG), particularly where applied to business workflows or integrated with structured data, experience working in regulated environments and implementing model governance frameworks (e.g. auditability, explainability, approvals, documentation standards), experience contributing to team standards, mentoring, or leading applied AI delivery.

• Attractive remuneration package based on qualifications and experience (including 13th salary and Discretionary Bonuses to reward exceptional performance);
• Opportunities to learn and grow through the Employee Training and Development program;
• Medical Insurance Cover, which includes Outpatient, Inpatient, and Dental Care;
• Multiple events to bond with the team and the group through Quarterly/Semestrial Team Activities for all the Company;
• Participation in welfare investment and savings plan through the Provident Fund Scheme;
• Birthday and Loyalty benefits;
• Collaboration with SportBenefit.