ML Pipeline & MLOps Engineer for GenAI & CV

Averis; Kuala Lumpur; Presencial; MYR 120.000 - 210.000; Jornada completa

Descripción de la vacante

Averis is hiring for an ML Pipeline Engineer to design and maintain production-grade ML pipelines for Computer Vision and GenAI applications. The role focuses on MLOps, automation, and scalable infrastructure across projects.

The ideal candidate has 3+ years in ML engineering, strong Python, and hands-on AWS (SageMaker, S3, ECR). Experience with Docker, CI/CD, and experiment tracking is essential for success in this role.

Formación

• 3+ years of experience in machine learning engineering and MLOps.
• Strong Python programming skills, with experience building maintainable and testable ML / data pipeline code.
• Hands-on experience with AWS services such as SageMaker, S3, ECR, or equivalent cloud ML infrastructure.
• Experience with Docker and CI/CD workflows for ML or data applications.
• Familiarity with experiment tracking, model versioning, and reproducible ML workflows.
• Good understanding of model training, evaluation and validation workflows.
• Strong communication and collaboration skills in cross-functional project environments.

Responsabilidades

• ML Pipeline Engineering: Build and maintain reproducible ML pipelines for Computer Vision and GenAI use cases.
• MLOps & Infrastructure: Build and maintain CI/CD workflows for ML code, including automated testing and containerization.
• Cross-project Engineering Support: Work across projects, contributing to shared ML engineering standards and reusable components.
• Labeling Support: Work with domain experts and annotators to deliver high quality labels for ML/CV projects.

Conocimientos

Python

AWS SageMaker

Docker

CI/CD

Experiment tracking

Model training

Data pipelines

Cross-functional collaboration

Herramientas

SageMaker

S3

ECR

Databricks

Descripción del empleo

Averis is hiring for an ML Pipeline Engineer to design and maintain production-grade ML pipelines for Computer Vision and GenAI applications. The role focuses on MLOps, automation, and scalable infrastructure across projects.

The ideal candidate has 3+ years in ML engineering, strong Python, and hands-on AWS (SageMaker, S3, ECR). Experience with Docker, CI/CD, and experiment tracking is essential for success in this role.