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.
Skills Required
- Experience in Automotive and B2B areas
- Proven experience managing machine learning models from development to production
- Strong understanding of the machine learning lifecycle
- Expertise in cloud platforms such as AWS, GCP, or Azure
- Experience with containerization and orchestration of ML pipelines
- Knowledge of infrastructure as code and CI/CD tools
- Solid understanding of machine learning algorithms
- Strong programming skills in Python
- Bachelor's degree in a relevant area of study
OneMagnify Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about OneMagnify and has not been reviewed or approved by OneMagnify.
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Parental & Family Support — Parental support is positioned as a defined benefit with six weeks of 100% paid parental leave plus employer-paid short-term disability. Parental leave sentiment is characterized as middling, but the policy itself is clearly specified as part of the package.
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Wellbeing & Lifestyle Benefits — Work flexibility is framed as a meaningful part of the total rewards experience through hybrid/remote options and flexible time off with paid sick leave. Added lifestyle-oriented perks such as volunteer PTO, pet insurance, legal assistance, ERGs, recognition programs, and partner discounts broaden the offering beyond core insurance.
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Flexible Benefits — A broad slate of optional add-ons and programs is described, including tuition reimbursement up to $5,250/year and other elective-style perks. This variety can help different employee needs be met even when base pay competitiveness is seen as only average.
OneMagnify Insights
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