Role and Responsibilities:

• Deliver production ML software models and components that solve real-world business problems, while working in collaboration with product and application teams
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
• Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art, next generation big data and machine learning applications
• Leverage cloud-based architectures and technologies to deliver optimized ML models at scale
• Construct optimized data pipelines to feed ML models
• Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code
• Advocate for software and machine learning engineering best practices
• Function as a technical or project lead and while mentoring junior engineering talent

Required Skills and Experience:

• 5+ years of experience building, scaling, and optimizing enterprise-grade Machine Learning systems, using one or more of the frameworks such as scikit-learn, XGBoost, TensorFlow, PyTorch, and Spark, to perform a variety of predictive tasks on both structured and unstructured data 
• 3+ years of experience designing and building data-intensive, production-ready solutions using distributed computing frameworks such as Spark 
• 3+ years of experience developing and deploying Machine Learning solutions in a public cloud environment (AWS, Azure, or Google Cloud).  
• 5+ years of experience in data wrangling in rational databases using SQL 
• 3+ years of experience architecting cutting-edge MLOps systems in enterprise environments 
• 3+ years experience with object-oriented programming design best practices 

Preferred Skills and Experience:

• Services experience is a strong plus.  
• Cloud certification preferred is a strong plus.