About the role
What you will do
Design, implement, and optimize end-to-end recommendation pipelines, from data ingestion to model inference.
Build and maintain scalable ETL pipelines to support reliable and efficient data flows.
Develop, evaluate, and continuously improve ML models for recommendation systems.
Research, prototype, and implement state-of-the-art (SOTA) approaches to improve recommendation quality and drive key business metrics.
Scale and optimize data and model pipelines to handle large volumes of data and real-time or batch processing needs.
Integrate multi-modal data (e.g., behavioral, transactional, and contextual signals) from various systems into recommendation models.
Ensure robustness and stability of pipelines by implementing unit and integration tests across data, modeling, and deployment workflows.
Monitor and maintain end-to-end system performance, including data pipelines, model quality, and downstream impact.
Design and analyze A/B tests to evaluate model performance and support data-driven product decisions.
Build dashboards and observability tools to track model metrics, system health, and business KPIs.
Collaborate closely with Data Engineers, Software Engineers, and stakeholders to deliver scalable, production-ready solutions.
What you bring
Bachelor's or Master's degree in Computer Science, Engineering, or a related field
Strong Python experience with recent production use, including hands-on work with data science and machine learning libraries and frameworks (e.g., Pandas, Polars, NumPy, scikit-learn, PyTorch, TensorFlow, JAX, Hugging Face, …)
Experience building and deploying end-to-end machine learning systems on cloud AI platforms (Azure, GCP, or AWS), from ETL pipelines to deployment and monitoring, including model versioning and experiment tracking, supporting either batch or real-time workflows.
Strong understanding of deep learning–based recommender systems for next-item prediction, and analogous NLP architectures that model sequential patterns and context
Demonstrated experience building efficient data transformation pipelines for both transactional (OLTP) and analytical (OLAP) workloads, with strong knowledge of SQL and NoSQL databases (e.g., PostgreSQL, MySQL, Redshift, Snowflake, BigQuery, MongoDB, Cassandra)
Experience with unit and integration testing (e.g., Pytest), CI/CD pipelines, and Docker-based containerization
What sets you apart
Experience building large-scale recommender systems (e.g., candidate generation, ranking, retrieval, personalization).
Track record of publications in deep learning at relevant conferences or journals.
Experience with Azure Data Factory / AWS Glue / Google Cloud Dataflow.
Experience designing and analyzing A/B tests, with a solid understanding of relevant evaluation metrics.
Experience designing and implementing metadata-driven pipelines to scale automated A/B testing systems.
Experience developing multi-modal models that integrate multiple data types (e.g., text, images, audio).
Experience applying transformer-based models or large language models (LLMs) to recommendation or personalization tasks.
Experience with distributed training, including data parallelism and model parallelism.
Experience with distributed data processing and big data technologies (e.g., Spark, Hadoop, Flink, Kafka, Hive, Presto, Databricks).
About us
ARRISE is a global leader in software development and services for the iGaming industry, bringing together more than 2,500 professionals operating across more than 12 locations worldwide.
From concept through to delivery, we combine technical expertise, creativity and industry knowledge to develop products, platforms and services that shape player experiences and support some of the industry's leading brands.
We are best known for the work we deliver for Pragmatic Play, creating and supporting technology, products and specialists services enjoyed by millions of players worldwide.
Originally posted on Himalayas