Job Details

Build and maintain full machine learning lifecycles including experiment tracking, model governance, deployment, and monitoring using tools like M - Lflow, Databricks, DS - Py, Lang. Graph, and Azure DevOps. Assemble and transform large, complex datasets from Databricks, Postgre. SQL, Apache-based sources, and other structured/unstructured systems, ensuring scalability and performance in production environments. Collaborate with data science, ML, and platform teams to build graph-based or modular workflows (e.g., Lang. Graph), ML pipelines (e.g., in Databricks), and integration with DevOps and GitLab systems. Ensure ML models are scalable, reproducible, and well-documented, leveraging ML - Ops tooling and open standards. Also responsible for other Duties/ Projects as assigned by business management as needed. Education and Work Experience:Bachelor's Degree Computer Science, Statistics, Informatics, Information Systems, Machine Learning, or another quantitative field (Required)Master's/...Machine Learning, AI, Data Science, Engineer, Computer Science, Data Engineer, Technology