Job VC
Senior Python Engineer (AI-driven Recommendation Platform, iGaming domain)
Technologies
Description
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.
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.