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JobDescription : The Impact The Lead Machine Learning Engineer is a member of S&P Global's AI Engineering group. In the AI Engineering group, machine learning engineers partner with data scientists to design, build, deploy, and support AI-powered applications that transform S&P Global's products and operations using machine learning and related techniques. Our machine learning engineers take a modern, full-stack approach to prototyping, developing, deploying, and operating advanced analytics applications and infrastructure in production environments. Working in an organization for which data is the primary raw material, finished product, and core asset provides an unusually rich environment for a machine learning engineer to make an impact.
Partner with data scientists to identify, prototype, develop, deploy, and operate AI-powered applications in production settings
Manage and support the organization's cloud-based data and computing platforms and infrastructure for AI applications
Help drive the organization's initiatives around topics such as data pipelines, DevOps, and cloud infrastructure and architecture
Bachelor's degree in a technical, engineering, or related field
5+ years experience in a data engineering, data science, data architecture, data product development, or related role
Expertise in building and supporting data pipelines and platforms based on SQL, NoSQL, distributed, streaming, and/or graph data technologies
Experience working in cloud-based architecture, such as AWS, Azure, and/or Google Cloud Platform
Familiarity with data science techniques such as machine learning, natural language processing, statistical data analysis, and data visualization
Experience developing, deploying, and orchestrating containerized applications using technologies such as Docker, Kubernetes, and/or Docker Swarm
Graduate degree in a technical, engineering, or related field
Experience with applied machine learning tools such as scikit-learn, TensorFlow, PyTorch, SparkML, or similar
Experience with operationalizing machine learning applications in a production setting