About the company:

We are hiring for a product iGaming company building a B2B SaaS platform that personalizes casino content in real time using machine learning. The product helps operators increase player engagement, retention, and revenue through smarter recommendations and tailored user experiences.

The company’s mission is to help gambling businesses scale efficiently by improving operations and raising the quality of the player journey through data-driven features.

Responsibilities:

• Maintain and evolve an existing recommendation system in a multi-tenant environment

• Bring models from experimentation into production and improve their real-world performance

• Build and support end-to-end ML pipelines: training, validation, deployment, retraining

• Optimize training/inference workloads for latency, reliability, memory usage, and scalability

• Integrate ML inference into Python-based backend services and collaborate with backend/data teams

• Define and track model KPIs linked to product and business impact (engagement/retention/revenue)

• Improve data quality, feature availability, and feature pipelines (Airflow-based workflows)

• Set up monitoring for model health: drift, degradation, anomalies, incident response

• Run controlled experiments (A/B tests), analyze results, and translate insights into improvements

• Document model behavior, assumptions, and operational runbooks

• Contribute to architecture decisions for scalable ML infrastructure and deployment practices

Requirements:

• 5+ years in ML Engineering / Production ML roles

• Degree in a quantitative field (Math/Stats/CS or similar)

• Strong Python skills and experience building production-grade ML services

• Solid ML foundation: supervised learning, ranking/recommendations, evaluation methodology

• Hands-on experience with feature engineering for event/behavioral data

• Production deployment experience: APIs and/or batch jobs, Airflow, CI/CD, containers

• Practical SQL knowledge and understanding of data access patterns

• Experience with monitoring ML systems end-to-end: data quality + model performance + alerts

• Understanding of experimentation and statistics (A/B testing, experiment design)

• Strong engineering habits: testing, code review, documentation

• Computer science fundamentals (processes, memory, performance considerations)

Nice to Have:

• Experience with multi-tenant architectures

• Kubernetes (K8s) and production platform tooling

• Prior iGaming/high-load personalization products background

Working Conditions:

• Location: worldwide

• Language: Russian-speaking required

• Remote-first, with an option to visit an office in Warsaw (not mandatory)

• Benefits package includes wellness program, medical compensation, sports support, paid sick leaves,
  21 vacation days + personal days, learning budget, English club, equipment provided, workplace setup bonus, team events, etc.