Lead Machine Learning Engineer Lead Machine Learning Engineer …

S&P Global
in Richmond, VA, United States
Permanent, Full time
Be the first to apply
Competitive
S&P Global
in Richmond, VA, United States
Permanent, Full time
Be the first to apply
Competitive
Lead Machine Learning Engineer
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.

Responsibilities
  • 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

Basic qualifications
  • 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
  • Experience in full-stack development with Python, JavaScript, .NET, Scala, and/or Java
  • 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
  • Entrepreneurial spirit

Preferred qualifications
  • 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


Close
Loading...