Machine Learning Engineer

Playtika is looking for a Senior 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 proven 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 or other AI systems, impacting millions of users.
• Transform complex data science prototypes into resilient, 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 tooling to empower AI-driven development workflows and modern workspaces.
• Implement end-to-end observability, tracing, and alerting for production ML systems.
• Collaborate closely with data scientists, product managers, architects, and cross-functional engineering teams.
• Stay up to date with machine learning, serving frameworks, and GenAI developments across the industry.

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.
• High 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 (Docker, Kubernetes) and utilizing model serving frameworks.
• A highly self-driven autodidact who is passionate about adopting innovative technologies.
• A team player who thrives in strategic projects, actively participates in decision-making, and collaborates effectively with business and technical functions.
• Big Advantage: Real-world production experience with personalization architectures, recommendation systems, or Reinforcement Learning models.