ML Ops Support

MOL Ops Engineer R2037 for Ford Direct

ML Ops Support

• 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
• Bachelors 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.