About the Role
Tickmill is looking to hire a Senior AI/ML Engineer (AI Lead) to join our rapidly expanding team. The ideal candidate will be a highly hands-on and business-oriented professional, capable of designing and delivering production-grade AI systems that drive measurable business impact.
Responsibilities
•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
Requirements
•5–8+ years of experience building and deploying machine learning models in production environments (not just experimentation)
•Strong Python programming skills and solid software engineering fundamentals (testing, code quality, modular design, maintainability)
•Strong understanding of machine learning concepts, model evaluation, feature engineering, and practical considerations in production systems (e.g. data leakage, drift, stability)
•Hands-on experience with large-scale data processing (Spark / PySpark)
•Experience with ML lifecycle tools (MLflow or similar) for experiment tracking, model management, and reproducibility
•Experience building and maintaining CI/CD pipelines (GitHub Actions preferred) for ML or data workflows
•Strong SQL skills and experience working with large, complex datasets in real-world environments
•Proven ability to deliver AI/ML solutions with measurable business impact, not just model performance improvements
•Experience working with model deployment, monitoring, drift detection, and retraining strategies in production systems
•Strong communication skills with the ability to work effectively with both technical and non-technical stakeholders, translating trade-offs clearly
•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
Conditions
•A Unique Opportunity for a career in a global, fast-growing company
•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 & 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 the welfare investment and savings plan through the Provident Fund Scheme
•Birthday and Loyalty benefits
•Collaboration with SportBenefit
