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 current technologies to improve performance, maintainability,

and reliability of production models including recent 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
Solid 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.

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