NLP & ML Architect — Remote, Scalable AI Solutions

Quebec Solution Inc; United States; Remote; USD 165,000 - 276,000; Part time

Job summary

Quebec Solution Inc. is seeking an experienced ML Architect to design, build, and lead scalable machine learning solutions on a remote contract basis. The role emphasizes architecture ownership, model development, MLOps, cloud platforms, and production deployment.

Collaborate with Data Scientists, Data Engineers, Software Engineers, and business teams to establish governance, monitoring, and secure, scalable pipelines. Relevant cloud experience and CI/CD are required for success.

Qualifications

• Extensive experience in ML architecture and production ML systems.
• Proficient in Python and ML frameworks (TensorFlow, PyTorch).
• Experience with ML pipelines and MLOps practices.
• Familiarity with cloud platforms (AWS, Azure, GCP).
• Knowledge of Docker, Kubernetes, CI/CD, and Git.

Responsibilities

• Design scalable and production-ready ML architectures and solutions.
• Lead development and deployment of machine learning models.
• Work with supervised, unsupervised, and deep learning techniques.
• Develop ML pipelines for data preparation, training, validation, and deployment.
• Implement MLOps practices for model lifecycle management.
• Optimize model performance, scalability, and reliability.
• Integrate ML models with APIs and enterprise applications.
• Collaborate with Data Scientists, Data Engineers, Software Engineers, and business teams.
• Establish best practices for model monitoring, governance, security, and versioning.
• Support cloud-based ML solutions and CI/CD pipelines.

Skills

ML architecture

Python

TensorFlow/PyTorch

ML pipelines

AWS/Azure/GCP

Docker/Kubernetes

CI/CD

APIs & enterprise apps

Communication

Tools

Docker

Kubernetes

CI/CD

Git

TensorFlow

PyTorch

Job description

Quebec Solution Inc. is seeking an experienced ML Architect to design, build, and lead scalable machine learning solutions on a remote contract basis. The role emphasizes architecture ownership, model development, MLOps, cloud platforms, and production deployment.

Collaborate with Data Scientists, Data Engineers, Software Engineers, and business teams to establish governance, monitoring, and secure, scalable pipelines. Relevant cloud experience and CI/CD are required for success.