ML Ops Support

MOL Ops Engineer R2037 for Ford Direct

ML Ops Support
(1) Job Description
(A) Experience in Automotive and B2B areas. Designing the data pipelines and engineering infrastructure enterprise machine learning systems at scale
(B) Take offline models data scientists build and deploy them into machine learning production system using Databricks
(C) Identify and evaluate new technologies to improve performance, maintainability, and reliability of production models including new features in Databricks
(D) Apply software engineering rigor and best practices to machine learning, including
CI/CD, automation, etc.
(E) Support model development, with an emphasis on auditability, versioning, and data security
(F) Facilitate the development and deployment of proof-of-concept machine learning systems
(G) Communicate across technical and business teams to build requirements and track progress

(2) Job Qualifications for MLOPS Engineer : -
(A) Proven experience managing machine learning models from development to production, including model deployment, monitoring, retraining, and scaling
(B) Strong understanding of the machine learning lifecycle, including model versioning, and continuous integration/continuous delivery (CI/CD) for ML models
(C) Expertise in cloud platforms such as AWS, GCP, or Azure for managing scalable ML infrastructure
(D) Experience with containerization (Docker, Kubernetes) and orchestration of ML pipelines
(E) Knowledge of infrastructure as code (Terraform, CloudFormation) and CI/CD tools
(Jenkins, GitLab, etc.).
(F) Solid understanding of machine learning algorithms, data preprocessing, and feature engineering.
(G) Experience with ML frameworks and libraries
(H) Strong programming skills in Python and familiarity with data engineering pipelines.
(3) Education and Experience
(A) Bachelor’s degree from a four-year college or university in Information
Management, Computer Science or Business Administration or a relevant area of study
(B) (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.