5 months ago
Remote, Poland or Warsaw, PolandStaff+
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, and retraining.
- Implement monitoring for model performance, data drift, data quality, and business impact.
- Apply explainability and feature-attribution techniques such as SHAP in business-critical and regulated contexts.
- Define model governance, documentation, versioning, auditability, and risk-management practices.
- Collaborate with Data Engineering on data pipelines, feature engineering, reproducibility, and scalable data foundations.
- Partner with Product, Risk, Commercial, and other stakeholders to deliver pragmatic AI solutions.
- 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.
- 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, with GitHub Actions preferred.
- Strong SQL skills and experience working with large, complex 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 and stakeholder-collaboration skills.
- Preferred experience includes fintech, trading, financial services, real-time or streaming ML systems, LLMs, embeddings, retrieval-augmented generation, regulated environments, model governance, mentoring, or applied AI leadership.
Benefits
- Career opportunity with a global, fast-growing company.
- Attractive remuneration package based on qualifications and experience.
- Employee Training & Development program.
- Hybrid work flexibility.
- Team and group bonding events.
- Birthday and loyalty benefits.
- Role based in Poland within Tickmill’s international organization.
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.