4 months ago
Tallinn, EstoniaStaff+
Responsibilities
- Design, develop, validate, deploy, monitor, and retrain production machine learning models end to end.
- Lead AI use cases including client lifetime value, churn prediction, and fraud or abuse detection.
- Build MLOps practices covering deployment pipelines, environment promotion, lifecycle management, monitoring, drift detection, and retraining.
- Implement model explainability, governance, documentation, versioning, auditability, and risk-appropriate approval processes.
- Partner with Data Engineering to support data pipelines, feature engineering, reproducibility, and scalable data foundations.
- Collaborate with Product, Risk, Commercial, and other stakeholders to deliver pragmatic AI solutions with measurable business impact.
- Mentor team members and promote best practices in production AI, MLOps, and applied machine learning.
Requirements
- 5–8+ years of experience building and deploying machine learning models in production environments.
- Strong Python programming skills and solid software engineering fundamentals, including testing, code quality, modular design, and maintainability.
- Strong knowledge of machine learning concepts, model evaluation, feature engineering, data leakage, drift, and model 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, preferably with GitHub Actions.
- Strong SQL skills and experience with large, complex real-world datasets.
- Demonstrated ability to deliver AI/ML solutions with measurable business impact.
- Experience with production model deployment, monitoring, drift detection, and retraining strategies.
- Strong communication skills and the ability to explain technical trade-offs to technical and non-technical stakeholders.
- Fintech, trading, financial services, real-time or streaming ML, LLMs, embeddings, retrieval-augmented generation, regulated environments, model governance, mentoring, or applied AI leadership experience is a plus.
Benefits
- Competitive remuneration package based on qualifications and experience.
- Employee Training & Development program with opportunities to learn and grow.
- Stebby Sports Compensation or Confido Health Compensation.
- Multiple team and group bonding events.
- Birthday and loyalty benefits.
- Estonia-based role with a recruitment process consisting of HR interview, assessment task, hiring-manager interview, and final management interview.
Tech Stack
Categories
About Tickmill
Tickmill is a privately held, multi-asset brokerage providing retail and professional traders access to forex, CFDs on stocks, indices, commodities, cryptocurrencies, and bonds, plus exchange-traded derivatives such as futures and options. It earns revenue through spreads, commissions, and financing on trades executed via its electronic platforms. Founded in 2014 and headquartered in London, the group operates through multiple regulated entities serving clients globally.