4 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
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