Senior Python Engineer

Senior Python Engineer (AI-driven Recommendation Platform, iGaming domain)

About the product
AI-powered performance improvement platform for online casinos and sportsbooks.
The product helps operators increase key business metrics by enhancing player engagement and loyalty through personalized recommendations and predictive analytics.

The platform aggregates and analyzes player behavior data across multiple client platforms, builds detailed player profiles, and provides insights and recommendations such as:

• identifying users who exploit bonus systems without long-term engagement,
• detecting patterns of risky or excessive play to promote responsible gaming,
• recommending relevant games and activities based on user preferences and history.

You’ll be part of a team building scalable recommendation systems, working with large datasets, and applying ML-driven personalization to improve the user experience across iGaming products.

Requirements

• 5+ years of experience in backend development with Python
• Strong background in math, statistics, or computer science
• Experience with FastAPI / Django / Flask
• Solid understanding of data structures, algorithms, and distributed systems
• Experience with SQL and NoSQL databases (PostgreSQL, Redis, DynamoDB, MongoDB, etc.)
• Practical knowledge of Kafka / RabbitMQ and asynchronous architectures
• Cloud experience (AWS — Lambda, S3, RDS, SQS, etc.)
• Experience building high-load and scalable systems
• English — (B2)

Nice to have

• Experience with recommendation systems, data analysis, or ML-driven personalization
• Familiarity with data pipelines and ETL processes
• Experience with Kubernetes, Docker, CI/CD
• Familiarity with Elasticsearch or data visualization tools (Grafana, Kibana)

Responsibilities

• Design and develop scalable backend components for AI-driven recommendation systems
• Work closely with data scientists and ML engineers to integrate models into production
• Optimize data retrieval, storage, and processing for large user datasets
• Ensure system stability, performance, and scalability
• Participate in architectural decisions, code reviews, and mentoring
• Contribute to building new features that improve player engagement and retention

Tech stack

Python, FastAPI, Django, Flask, Kafka, RabbitMQ, PostgreSQL, Redis, DynamoDB, AWS (Lambda, S3, RDS, API Gateway, CloudWatch), Kubernetes, Pandas, Numpy, SQLAlchemy, Celery, Elasticsearch, Prometheus, Grafana.