Remote AI/ML Engineer: Build & Deploy Scalable Models

Quik Hire Staffing; United Arab Emirates; On-site; AED 734,000 - 1,836,000; Full time

Job summary

Quik Hire Staffing is seeking an AI/ML Engineer to design, train, and deploy machine learning models powering core product features. This remote, full-time role involves collaborating with cross-functional teams to build scalable AI solutions that enhance user experience and drive operational efficiency.

You'll work with Python, TensorFlow, PyTorch, and modern MLOps practices across AWS, GCP, or Azure, with emphasis on reliable data pipelines, model evaluation, and production-grade deployment.

Qualifications

• Proficiency in Python, TensorFlow, and PyTorch for model development and training.
• Experience with MLOps tools for deployment and monitoring (MLflow, Kubeflow, SageMaker).
• Strong data preprocessing, feature engineering, and evaluation metrics understanding.
• Familiarity with cloud platforms (AWS, GCP, or Azure) for scalable AI infra.
• Knowledge of Git and CI/CD best practices.

Responsibilities

• Design, train, and optimize machine learning models using Python, TensorFlow, and PyTorch.
• Develop and maintain data pipelines to ensure high-quality input for model training and evaluation.
• Implement MLOps practices to streamline model deployment, monitoring, and version control.
• Collaborate with data scientists and software engineers to integrate AI capabilities into production systems.
• Conduct performance benchmarking and A/B testing to validate model accuracy and business impact.

Skills

Python

TensorFlow

PyTorch

Data preprocessing

Feature engineering

Model evaluation metrics

Cloud platforms

Git

CI/CD

Tools

MLflow

Kubeflow

SageMaker

Job description

Quik Hire Staffing is seeking an AI/ML Engineer to design, train, and deploy machine learning models powering core product features. This remote, full-time role involves collaborating with cross-functional teams to build scalable AI solutions that enhance user experience and drive operational efficiency.

You'll work with Python, TensorFlow, PyTorch, and modern MLOps practices across AWS, GCP, or Azure, with emphasis on reliable data pipelines, model evaluation, and production-grade deployment.