Machine Learning Engineer 3

The Sales & Servicing ML Engineering team as part of Consumer and Merchant Solutions (CMS) Team as part of the AI, ML Platform Solutions (AMPS) Org plays a critical role in leveraging machine learning and data-driven solutions to enhance sales processes, drive revenue growth, and improve customer experience. This team is responsible for developing, deploying, and maintaining machine learning models and data pipelines to address key challenges and give actionable insights in the sales domain.

• Design and build production grade ML applications from Data Preparation, Feature Engineering, Model Training and Evaluation, and Deployment.
• Highly effective at working in cross-functional groups including Data Scientist, ML Engineers, Software Engineers, QA Engineers and Product Managers to build and deploy ML and Gen AI based applications.
• Highly analytical, innovative, and able to think strategically and to develop detailed technical specifications based on Product Requirements Documents.

• Write clean, high-performance, high-quality, maintainable code in Python/Scala and SQL.
• Discovering, analyzing, structuring, and mining data
• Be a key engineer contributing to the design and development of the new functionalities and maintaining the existing components
• Work with data scientists and backend engineers to implement scalable AI and ML solutions to solve complex problems at scale.
• Maintain and enhance the existing architectural and design documentation and create new ones as needed.

• Excellent communication skills (both oral and written) to communicate highly technical work and decision to our Business, Data science partners, and Leadership
• Experience with data warehousing architecture and data modeling best practices.
• Proven experience in designing and deploying ML/AI solutions at scale in the payments or a similar domain.
• Strong proficiency in big data technologies such as Apache Spark, Hadoop, and distributed computing frameworks.
• Hands-on expertise with cloud-native ML tools and platforms, particularly Vertex AI, and experience deploying models in cloud environments (e.g., Google Cloud, AWS, or Azure).
• Excellent programming skills in Python, Scala, or Java, with experience in ML libraries such as TensorFlow, PyTorch, or Scikit-learn.
• Deep understanding of the end-to-end model development lifecycle, including data wrangling, feature engineering, model evaluation, and monitoring.
• Strong problem-solving and analytical skills, with a passion for tackling complex business challenges
• Familiar with data movement techniques and best practices to handle large volumes of data