AI Engineer Ops

OneMagnify; Chennai District; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

OneMagnify is seeking an AI Engineer, Ops to design scalable data pipelines and enterprise ML infra. You will deploy models to production using Databricks, evaluate new tech, and ensure auditability, versioning, and security across ML systems.

You will collaborate with data scientists, engineers, and business teams to align requirements and track progress. The role emphasizes hands-on ML operations, containerized environments, and cloud platforms (AWS/GCP/Azure).

Qualifications

• Experience managing ML models from development to production, including deployment, monitoring, retraining, and scaling.
• Strong understanding of the ML lifecycle, including model versioning and CI/CD for ML models.
• Expertise with cloud platforms (AWS, GCP, or Azure) for scalable ML infra.
• Experience with ML frameworks and libraries.
• Strong Python programming and data engineering pipelines.
• Data analytics or BI experience (7+ years) and analytics initiatives management (3+ years).

Responsibilities

• Design data pipelines and engineering infra for enterprise ML at scale.
• Deploy offline models into production via Databricks.
• Evaluate new technologies to improve performance, maintainability, reliability of production models.
• Apply software engineering rigor to ML, including CI/CD and automation.
• Support model development with auditability, versioning, and data security.
• Facilitate development and deployment of proof-of-concept ML systems.
• Communicate with technical and business teams to build requirements and track progress.

Skills

Python programming

ML production deployment

Data engineering pipelines

Auditability and data security

Communication with teams

Education

Bachelor’s degree in Information Management or Computer Science

Data analytics or BI experience

Tools

Docker

Kubernetes

Terraform

CI/CD tools (Jenkins, GitLab)

Job description

Job Description:

AI Engineer, Ops

• 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 AI Engineer, Ops

• 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.