Machine Learning Engineer
Role details
Job location
Tech stack
Job description
systems, streamlining ML operations, and creating tools that enhance the quality and efficiency of localized content. This role focuses on designing, deploying, and maintaining ML models and infrastructure, collaborating closely with Data Engineers and Data Scientists. Responsibilities - Design, build, and maintain scalable and production-ready ML pipelines to support AI-driven localization workflows. - Collaborate with cross-functional teams to understand business needs and translate them into ML solutions. - Train, evaluate, and fine-tune models for NLP, Computer Vision, and other ML use cases. - Deploy and monitor ML models in different environments, ensuring performance, scalability, and reliability. - Develop preprocessing pipelines tailored to ML/DL tasks by working with large structured and unstructured datasets in multiple languages. - Leverage MLOps best practices for versioning, testing, CI/CD, and monitoring of models (e.g., MLflow, Sagemaker, or VertexAI). - Design
Requirements
develop, and maintain API REST services using languages such as Python, .NET, and/or Node.js. - Partner with Data Engineers and Data Scientists to ensure efficient data access and optimized feature engineering processes. - Contribute to continuous model and system improvement through experiment tracking, feedback loops, and performance analysis. - Conduct code reviews and ensure high-quality coding standards. - Optimize applications for maximum speed and scalability. - Collaborate with cross-functional teams to define, design, and ship new features. - Ensure adherence to ethical AI and data governance standards. Qualifications - 2+ years of hands-on experience in Machine Learning Engineering. - Bachelor's degree in Computer Science, Engineering, Applied Mathematics, or related discipline. - Strong Python programming skills, with experience in ML libraries (scikit-learn, TensorFlow, PyTorch, Hugging Face). - Proficiency in building and deploying ML models in real-world applications. - Familiarity with data processing frameworks (Pandas, NumPy) and orchestration tools (Airflow, Prefect). - Solid understanding of model lifecycle management and MLOps tools (e.g., MLflow, VertexAI, SageMaker, AzureML). - Experience working with APIs, RESTful services, and microservice-based architecture. - Knowledge of NLP and Computer vision techniques and tools for multilingual data is a strong plus. - Experience with cloud services (AWS, Azure, or GCP) for ML/DL development and deployment. - Experience with WebAPI and RESTful services. - Knowledge of software engineering best practices and tools (Gitlab and Github), such as Continuous Integration and Version Control (Git). - Oversee and contribute to the underlying infrastructure that powers ML systems (e.g, Terraform) ensuring robust, maintainable, and secure foundations for scalable deployment. - Strong debugging skills and fluent in reading code. - Strong problem-solving skills, and ability to communicate technical concepts clearly with stakeholders. - Excellent communication and collaboration skills, with the ability to translate data insights into business impact. About Electronic Arts We're proud to have an extensive portfolio of games and experiences, locations around the world, and opportunities across EA. We value adaptability, resilience, creativity, and curiosity. From leadership that brings out your potential, to creating space for learning and experimenting, we empower you to do great work and pursue opportunities for growth. We adopt a holistic approach to our benefits programs, emphasizing physical, emotional, financial, career, and community wellness to support a balanced life. Our packages are tailored to meet local needs and may include healthcare coverage, mental well-being support, retirement savings, paid time off, family leaves, complimentary games, and more. We nurture environments where our teams can always bring their best to what they do. Electronic Arts is an equal opportunity employer. All employment decisions are made without regard to