We are seeking an experienced Machine Learning Operations (MLOps) Engineer to join our EA Loc Data & AI team. The team's mission is to support and accelerate the use of machine learning across EA by building robust, automated, and scalable ML systems. This includes creating and managing MLOps pipelines, automating the deployment of ML models, and ensuring their efficient lifecycle management. We are looking for a candidate with a strong background in MLOps, who is passionate about leveraging these techniques to solve complex real-world problems. As an MLOps Engineer at EA, you will collaborate with data scientists, data engineers, and business teams, driving the implementation of MLOps practices and sharing your expertise across the organization. Responsibilities: Design, build, and manage ML pipelines, from data extraction and preparation to model training, evaluation, and deployment. Model hyperparameter optimization. Model training optimization. Automate the ML pipeline using MLOps tools and practices and optimize it for scalability and performance. Monitor ML models in production, manage their lifecycle, and ensure their reliability and performance. Model version tracking & governance. Collaborate with data scientists, data engineers, and business teams to understand their needs, implement ML solutions, and drive the adoption of MLOps. Conduct complex data analysis and report on results. Keep abreast of the latest trends and advancements in MLOps and machine learning, and contribute to the continuous improvement of MLOps practices at EA. Mentor junior team members and foster a culture of learning and sharing within the team. Qualifications: Bachelor's or master's degree in Computer Science, Software Engineering, Statistics, Applied Mathematics, or a related field. Minimum of 2-3 years of experience in ML Engineering/ MLOps or similar roles. Extensive knowledge of machine learning algorithms and principles. Proficiency in programming frameworks commonly used in machine and deep learning as PyTorch, Jax or Keras Proficiency in code in Python Experience on deep learning training optimization libraries/techniques such as Deepspeed, LoRA, PEFT, etc. Experience with MLOps tools such as MLFlow, Kubeflow or Weights and Biases. Solid experience with MLOps practices and tools, such as CI/CD, IaC (Infrastructure-as-code) tools (like CloudFormation, Terraform), automated testing, model monitoring, and version control. Experience with cloud computing platforms like Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure. Strong ability to build partnerships and foster collaboration across teams. Proven track record of driving results and innovation in MLOps or machine learning. Interest in gaming is a big plus.
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