Electronic Arts

The EA Digital Platform (EADP) group is the core powering the global EA ecosystem. We provide the foundation for all of EA’s incredible games and player experiences with high-level platforms like Cloud, Commerce, Data and AI, Gameplay Services, Identity and Social. By providing reusable capabilities that game teams can easily integrate into their work, we let them focus on making some of the best games in the world and creating meaningful relationships with our players. We’re behind the curtain, making it all work together. Come power the future of play with us.

The Challenge Ahead:

As an AI Engineer II, you will lead the creation of a scalable recommendation system within our Live Services domain. You will report to the Technical Director of EADP's Data & AI Team.

Responsibilities

• You will architect and build an AI/ML live service platform that supports real-time applications, providing a foundation for teams to deploy and manage machine learning models.
• You will lead the design of a scalable, secure, and highly available platform that can handle diverse machine learning workflows and real-time data processing needs.
• You will build systems that support model development, training, and deployment, ensuring the smooth transition from development to production for real-time applications.
• You will design and implement cloud-based solutions, using platforms such as AWS, GCP, or Azure, to support scalable machine learning workloads and ensure high availability.
• You will develop tools and services that automate key processes such as data preprocessing, model training, deployment, and monitoring to refine operations across multiple live services.

Qualifications

• 3+ years of experience in software engineering, with a focus on AI/ML systems or platform development.
• Experience designing and building scalable cloud-based platforms for machine learning applications, within real-time, live service contexts.
• Proficiency in AI/ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
• Experience with cloud platforms (AWS, GCP, or Azure) and cloud-native tools for managing infrastructure.
• Hands-on experience deploying and managing machine learning models in production, for real-time applications.
• Proficiency in containerization and orchestration tools such as Docker and Kubernetes.
• Experience with vector databases (e.g., Weviate, Milvus) and graph databases (e.g., Neo4j, ArangoDB), in the context of machine learning systems.
• Expertise in Python, Go, or similar languages commonly used in AI/ML development.
• Expertise in distributed systems, data pipelines, and real-time data processing technologies.