ML Ops Support Engineer

About the role

ML Ops Support Engineer (R2037) for Ford Direct in Chennai, designing data pipelines and engineering infrastructure for enterprise machine learning systems at scale. The role takes offline models built by data scientists and deploys them into production using Databricks, applies software engineering rigor including CI/CD and automation, and supports model development with an emphasis on auditability, versioning, and data security. The position also involves identifying new technologies to improve performance, maintainability, and reliability, facilitating proof-of-concept ML systems, and communicating across technical and business teams to build requirements and track progress.

What you’ll do

• Design data pipelines and engineering infrastructure for enterprise machine learning systems at scale
• Take offline models data scientists build and deploy them into machine learning production systems 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 and automation
• 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
• Experience in Automotive and B2B areas

What you’ll bring

• Bachelor's degree from a four-year college or university in Information Management, Computer Science, Business Administration, or a relevant area of study
• Data analytics or business intelligence experience (7 years)
• Model development, monitoring and production experience (5+ years)
• Management of analytics initiatives (3+ years)
• Proven experience managing machine learning models from development to production, including deployment, monitoring, retraining, and scaling
• Strong understanding of the machine learning lifecycle, including model versioning and 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

Skills

Education

Bachelor’s degree from a four-year college or university in Information Management, Computer Science or Business Administration or a relevant area of study