A global leader in the Software Development industry is hiring a Machine Learning (AI) Engineer for a hybrid/remote role open to candidates in the UAE, developing and deploying machine learning models that power core product features, collaborating with cross-functional teams to design scalable AI solutions, with a payout range of $200K-$500K/yr.
About the Opportunity — AI-Driven Product Engineering
Role Title: Machine Learning (AI) Engineer
Work Format: Hybrid, primarily remote
Compensation: $200,000 – $500,000 per year
Core Frameworks: Python, TensorFlow, and PyTorch
MLOps Toolset: MLflow, Kubeflow, or SageMaker for deployment and monitoring
Career Impact & High-Value Opportunity
Global Reach: Remote-first role open to qualified candidates across the UAE and beyond
Product Impact: Power core product features used at scale for real users
Top-Tier Compensation: Among the highest salary ranges available in remote AI/ML engineering
Career Growth: Access to cutting-edge tools, datasets, and complex technical challenges
Position Overview
We are hiring for one of our clients, seeking an AI/ML Engineer to develop and deploy machine learning models that power core product features. The role involves collaborating with cross-functional teams to design scalable AI solutions that enhance user experience and operational efficiency, working with a global leader in the Software Development industry.
Why This Role Matters:Â As Machine Learning Engineer, you design and train models that directly power core product features at scale, work hybrid/remote with an exceptional $200K-$500K/yr compensation range, apply Python, TensorFlow, and PyTorch expertise alongside modern MLOps tools like MLflow, Kubeflow, and SageMaker, collaborate with data scientists and software engineers on production-grade AI systems, validate model impact through rigorous A/B testing and performance benchmarking, and gain access to cutting-edge tools and datasets within a global software development leader.
Key Responsibilities
Model Design & Training
- Design, train, and optimize machine learning models using Python, TensorFlow, and PyTorch frameworks
- Develop and maintain data pipelines to ensure high-quality input for model training and evaluation
MLOps & Production Deployment
- 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
Performance Validation & Business Impact
- Conduct performance benchmarking and A/B testing to validate model accuracy and business impact
- Apply data preprocessing and feature engineering techniques to improve model evaluation metrics
 Qualifications & Requirements
Essential Skills
- Proficiency in Python, TensorFlow, and PyTorch for model development and training
- Experience with MLOps tools such as MLflow, Kubeflow, or SageMaker for deployment and monitoring
- Strong background in data preprocessing, feature engineering, and model evaluation metrics
- Familiarity with cloud platforms (AWS, GCP, or Azure) for scalable AI infrastructure
- Knowledge of software engineering best practices, including version control (Git) and CI/CD pipelines
About the Opportunity
This role offers a unique opportunity to work with a global leader in the Software Development industry, contributing to the advancement of AI-driven product solutions. The position involves solving complex technical challenges in a collaborative environment with access to cutting-edge tools and datasets, all within a hybrid/remote structure and an exceptional $200K-$500K/yr compensation range. All qualified candidates are welcome regardless of background, experience, or prior employment history, with applications reviewed solely on demonstrated technical ability.
Career Excellence: Build a top-tier machine learning engineering career with a global software leader, working on AI-driven product solutions with flexible hybrid arrangements and industry-leading pay.
Who Should Apply?
- Machine Learning Engineers:Â With hands-on Python, TensorFlow, and PyTorch expertise
- MLOps Specialists:Â Experienced with MLflow, Kubeflow, or SageMaker deployment pipelines
- Cloud AI Infrastructure Engineers:Â Comfortable scaling ML systems on AWS, GCP, or Azure
- Product-Focused Data Scientists:Â Interested in translating models into real user-facing features
- Remote-First Senior Engineers:Â Seeking top-tier compensation in a flexible, hybrid role
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