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Specialized MLOps skillset but metro location and mid-level seniority increase applicant density.
Requires specialized MLOps and Databricks experience, limiting cross-industry portability.
Explicit multi-year requirements and mandatory cloud, Databricks, IaC, and container orchestration skills.
Design and manage data pipelines and infrastructure for enterprise-scale machine learning systems primarily using Databricks.
Deploy offline machine learning models into production systems, ensuring model versioning, auditability, and data security.
Evaluate and implement new technologies to enhance performance, maintainability, and reliability of production ML models with a focus on CI/CD and automation.
Work Experience Required: 5+ years in model development, monitoring, and production; 7 years in data analytics or business intelligence; 3+ years managing analytics initiatives.
Bachelor’s degree in Information Management, Computer Science, Business Administration, or related field.
Experience in automotive and B2B sectors with strong expertise in ML lifecycle, including deployment, monitoring, and scaling models.
Proficient with cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), infrastructure as code (Terraform, CloudFormation), CI/CD tools (Jenkins, GitLab), and programming in Python.
Experienced in end-to-end machine learning operations with emphasis on scalable production environments and automation.
Skilled at bridging technical and business teams to translate requirements into ML deployment and monitoring solutions.
Background in automotive or B2B domains, comfortable working with enterprise ML systems using Databricks and cloud-native technologies.