BSc or MSc in Computer Science, Engineering, or a related technical field.
5+ years of experience as an ML Engineer, Backend Engineer, or similar, with a track record of delivering scalable production solutions.
Proficiency in Python and modern API development (FastAPI, Pydantic).
Hands-on experience with streaming and data processing workloads (Apache Kafka, PySpark, Airflow/MLflow/Kubeflow).
Experience deploying containerized applications (Dock

Key requirements

BSc or MSc in Computer Science, Engineering, or a related technical field.
5+ years of experience as an ML Engineer, Backend Engineer, or similar, with a track record of delivering scalable production solutions.
Proficiency in Python and modern API development (FastAPI, Pydantic).
Hands-on experience with streaming and data processing workloads (Apache Kafka, PySpark, Airflow/MLflow/Kubeflow).
Experience deploying containerized applications (Dock

Jobs at Playtika

1 active role from this employer in the JobMatcher catalog.

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Jobs at Playtika

1 active role from this employer in the JobMatcher catalog.

All company jobs

About the role

Playtika is looking for a Machine Learning Engineer to join our team and build efficient, data-driven AI systems that power our core personalization infrastructure. The ideal candidate has a strong backend engineering background and hands-on experience designing, deploying, and maintaining high-throughput machine learning architectures using Python.

Responsibilities

Design, build, and maintain large-scale ML serving architectures for real-time personalization and other AI systems, impacting millions of players.
Transform prototypes into production-ready infrastructure and integrate algorithms and GenAI models into production environments.
Develop robust streaming pipelines to support continuous model inference and training workflows.
Develop and maintain AI agents and internal tools to enable AI-driven development workflows.
Implement end-to-end observability, tracing, and alerting for production ML systems.
Collaborate with data scientists, product managers, architects, and cross-functional engineering teams.
Stay up to date with ML serving frameworks and GenAI developments.

Requirements

BSc or MSc in Computer Science, Engineering, or a related technical field.
5+ years of experience as an ML Engineer, Backend Engineer, or similar, with a track record of delivering scalable production solutions.
Proficiency in Python and modern API development (FastAPI, Pydantic).
Hands-on experience with streaming and data processing workloads (Apache Kafka, PySpark, Airflow/MLflow/Kubeflow).
Experience deploying containerized applications (Dock

About the role

Playtika is looking for a Machine Learning Engineer to join our team and build efficient, data-driven AI systems that power our core personalization infrastructure. The ideal candidate has a strong backend engineering background and hands-on experience designing, deploying, and maintaining high-throughput machine learning architectures using Python.

Responsibilities

Design, build, and maintain large-scale ML serving architectures for real-time personalization and other AI systems, impacting millions of players.
Transform prototypes into production-ready infrastructure and integrate algorithms and GenAI models into production environments.
Develop robust streaming pipelines to support continuous model inference and training workflows.
Develop and maintain AI agents and internal tools to enable AI-driven development workflows.
Implement end-to-end observability, tracing, and alerting for production ML systems.
Collaborate with data scientists, product managers, architects, and cross-functional engineering teams.
Stay up to date with ML serving frameworks and GenAI developments.

Requirements

BSc or MSc in Computer Science, Engineering, or a related technical field.
5+ years of experience as an ML Engineer, Backend Engineer, or similar, with a track record of delivering scalable production solutions.
Proficiency in Python and modern API development (FastAPI, Pydantic).
Hands-on experience with streaming and data processing workloads (Apache Kafka, PySpark, Airflow/MLflow/Kubeflow).
Experience deploying containerized applications (Dock

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