Mid-Senior ML Engineer
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Job Description
ML Engineer responsible for building and operating data pipelines for AI systems in iGaming. Working with structured and unstructured data on platforms like Spark and Databricks.
Responsibilities:
- 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.
- 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
Benefits:
- 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.
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