ML Ops Support

MOL Ops Engineer R2037 for Ford Direct ML Ops Support - Job Description - Experience in Automotive and B2B areas. Designing the data pipelines and engineering infrastructure enterprise machine learning systems at scale - Take offline models data scientists build and deploy them into machine learning production system using Databricks - Identify and evaluate new technologies to improve performance, maintainability, and reliability of production models including new features in Databricks - Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc. - Support model development, with an emphasis on auditability, versioning, and data security - Facilitate the development and deployment of proof-of-concept machine learning systems - Communicate across technical and business teams to build requirements and track progress - Job Qualifications for MLOPS Engineer : - - Proven experience managing machine learning models from development to production, including model deployment, monitoring, retraining, and scaling - Strong understanding of the machine learning lifecycle, including model versioning, and continuous integration/continuous delivery (CI/CD) for ML models - Expertise in cloud platforms such as AWS, GCP, or Azure for managing scalable ML infrastructure - Experience with containerization (Docker, Kubernetes) and orchestration of ML pipelines - Knowledge of infrastructure as code (Terraform, CloudFormation) and CI/CD tools (Jenkins, GitLab, etc.). - Solid understanding of machine learning algorithms, data preprocessing, and feature engineering. - Experience with ML frameworks and libraries - Strong programming skills in Python and familiarity with data engineering pipelines. - Education and Experience - Bachelor’s degree from a four-year college or university in Information Management, Computer Science or Business Administration or a relevant area of study - (C) (D) (E) Data analytics or business intelligence experience (7 years). Model development, monitoring and production (5+ years). Management of analytics initiatives (3+ years). Experience with various data analytics tools.