Middle to Senior ML Engineer

About us
We are a product R&D company that creates solutions for the dynamic iGaming Ecosystem.
Our mission is to build cutting-edge platforms that reinvent the iGaming industry.

About the team

Small engineering team building production AI systems in the iGaming industry. We work across personalization, recommendation, fraud detection, and GenAI — processing millions of events daily through real-time, near-real-time, and batch pipelines. You'll have direct ownership over systems and the autonomy to shape how they evolve.

The role

You'll build and operate the data pipelines that power our AI systems — streaming, near-real-time, and batch. You'll work with high-volume structured and unstructured data on platforms like Spark and Databricks, building the infrastructure that connects raw data to production models across personalization, fraud, and GenAI use cases.

 
Responsibilities

• Build and maintain ML inference and feature engineering pipelines on Databricks
• Develop feature pipelines — transformations, aggregations, time-based features at scale using PySpark
• Deploy and manage models in production — versioning, registry, serving (MLflow)
• Build batch inference jobs that generate predictions at scale
• Integrate ML outputs with streaming infrastructure (Kafka) for downstream consumption
• Optimize SQL queries across multiple engines (Databricks SQL, PostgreSQL)
• Monitor data quality, pipeline reliability, and model serving health
• Collaborate with ML scientists to productionize their research

 
Requirements

• 3+ years of professional Python development in a data or ML engineering context
• Strong PySpark experience — writing, debugging, and optimizing Spark jobs in production
• Hands-on experience with Databricks or similar managed Spark platform (EMR, Dataproc)
• Production experience with Kafka or equivalent streaming platform
• Solid SQL skills across multiple database engines
• Familiarity with ML model deployment and serving (MLflow, SageMaker, or equivalent)
• Familiarity with Kubernetes
• Understanding of feature engineering patterns and data pipeline design
• Strong analytical thinking and data intuition

Nice to have

• Exposure to recommendation systems or personalization platforms
• Experience with pipeline orchestration (Databricks Asset Bundles, Airflow, or similar)
• Familiarity with columnar processing libraries (Polars, pandas)
• Experience with ML observability — model performance monitoring, data drift detection, pipeline alerting iGaming domain experience
  

You will get:

• Work in a technically strong environment with modern stack and mature Agile culture;
• High autonomy, decision-making authority, and close cooperation with leadership;
• A position in a product development company with a dynamic environment and several concurrent projects;
• Opportunity to contribute (your ideas for improvement implementation);
• Continuous self-improvement and growth, including budget for certifications and courses;
• Competitive salary plus financial bonuses for performers;
• Company prepaid AI agent;
• Medical insurance coverage;
• English language courses;
• Wellbeing package: online-yoga classes, Yakaboo, BetterMe App: Health Coaching, BetterMe App: Mental Health;
• Corporate events and fun team-building activities;
• Remote-first culture.