Remote Part-Time ML Engineer - Vertex AI Specialist

Cogleus LLC; Indiana; A distancia; USD 83.000 - 131.000; A tiempo parcial

Descripción de la vacante

Cogleus LLC is seeking a hands-on ML Engineer (Part-Time, Remote) to design, develop, and deploy models on Google Vertex AI. You will implement NLP/classification tasks, data workflows, and ML pipelines in a production-ready environment for a scalable platform.

You will collaborate with engineers to integrate ML components, monitor models, retrain as needed, and contribute to architectural decisions to ensure performance and maintainability in a startup-driven setting.

Formación

• Proven experience with Google Vertex AI (training, deployment, pipelines, model monitoring).
• Strong Python skills and experience with ML frameworks (TensorFlow, PyTorch, scikit-learn, etc.).
• Experience in NLP and text classification.
• Familiarity with cloud-based data storage and retrieval (BigQuery, Cloud Storage).
• Strong understanding of ML lifecycle and MLOps best practices.

Responsabilidades

• Design, build, and deploy ML models on Google Vertex AI for NLP and classification tasks.
• Develop data ingestion, transformation, and storage workflows.
• Train, evaluate, and optimize models for performance and accuracy.
• Collaborate with backend developers to integrate ML components into the platform.
• Implement monitoring, retraining, and model versioning processes.
• Contribute to architecture decisions for scalability and maintainability.

Conocimientos

Google Vertex AI

Python

NLP

ML frameworks

ML lifecycle / MLOps

Herramientas

TensorFlow

PyTorch

scikit-learn

BigQuery

Cloud Storage

Descripción del empleo

Cogleus LLC is seeking a hands-on ML Engineer (Part-Time, Remote) to design, develop, and deploy models on Google Vertex AI. You will implement NLP/classification tasks, data workflows, and ML pipelines in a production-ready environment for a scalable platform.

You will collaborate with engineers to integrate ML components, monitor models, retrain as needed, and contribute to architectural decisions to ensure performance and maintainability in a startup-driven setting.