ML Ops Support
This role focuses on bridging data science and production systems by deploying ML models at scale using Databricks, while ensuring reliability, security, and maintainability in an automotive/B2B context. The candidate will collaborate across technical and business teams to optimize pipelines, automate workflows, and support end-to-end ML lifecycle with a focus on auditability and versioning.
Skills required
About this role
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.
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