OneMagnify
Website:
onemagnify.com
Job details:
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.
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