Machine Learning Engineer

Aeg - Atlanta, GA

Full-time

Atlanta, GA

Professional, Scientific, and Technical Services

About the position

The Machine Learning Engineer at AMB Sports & Entertainment will play a crucial role in enhancing fan experiences and optimizing business strategies through innovative technology and data-driven insights. This position involves designing, developing, and deploying machine learning models to address various business challenges, collaborating with cross-functional teams, and staying updated with the latest advancements in the field.

Responsibilities

• Design, develop, and deploy scalable machine learning models for predictive analytics, recommendation systems, and optimization problems.
• Collaborate with data engineers to build and maintain data pipelines for model training and production deployment.
• Stay updated on advancements in machine learning, deep learning, and AI to apply innovative solutions.
• Perform regular evaluation, fine-tuning, and optimization of models to meet performance benchmarks.
• Work closely with cross-functional teams to understand project requirements and deliver results.
• Develop tools to automate repetitive tasks in data preparation and model training.
• Write clean, maintainable code and ensure proper documentation for models and workflows.

Requirements

• 3-5 years of experience in designing, developing, and deploying machine learning models in a production environment.
• Strong proficiency in Python and experience with machine learning libraries such as TensorFlow, PyTorch, and Scikit-learn.
• Proficiency with SQL and experience with large-scale data systems like Hadoop, Spark, or Databricks.
• Experience with cloud platforms (AWS, Azure, Google Cloud Platform) for model deployment.
• Familiarity with deep learning, NLP, computer vision, or other advanced machine learning techniques.
• Strong problem-solving skills and ability to translate business needs into technical solutions.
• Proven ability to work collaboratively and communicate technical concepts to non-technical stakeholders.
• Experience with version control tools such as Git.

Nice-to-haves

• Experience deploying models via containers and microservices using Docker or Kubernetes.
• Familiarity with MLOps practices and CI/CD pipelines.
• Experience with data visualization tools like Tableau or Power BI.
• Industry experience in sports & entertainment.