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

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

About the Product

We are developing an AI-powered performance optimization platform for online casinos and sportsbooks. The solution helps operators improve key business metrics by increasing player engagement and loyalty through personalized recommendations and predictive analytics.

The platform collects and processes player behavior data across multiple systems, builds detailed user profiles, and generates actionable insights, including:

• identifying players who misuse bonus programs without long-term retention
• detecting patterns of risky or excessive gaming to support responsible play
• recommending relevant games and activities based on user preferences and behavioral history

You will join a team building scalable recommendation systems, working with large volumes of data, and implementing ML-driven personalization to enhance user experience within iGaming products.

Requirements

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

Nice to Have

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

Responsibilities

• Design and develop scalable backend services for AI-powered recommendation systems
• Collaborate with data scientists and ML engineers to deploy models into production
• Optimize data storage, processing, and retrieval for large-scale user datasets
• Ensure system reliability, performance, and scalability
• Take part in architectural discussions, code reviews, and mentoring
• Contribute to new features aimed at increasing 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.