AI Engineer - Machine Learning Systems

AI Engineer
- Machine Learning Systems Job Snapshot Role: AI Engineer
- Machine Learning Systems

Location:

Abu Dhabi Emirate, United Arab Emirates Industry: IT and Services Function:
Software Engineering / Web Development

Experience:

Proven experience developing and deploying production machine learning systems Job Type: Contract
- Remote

Position Overview AI Engineer
- Machine Learning Systems in Abu Dhabi Emirate, United Arab Emirates is a fully remote IT and Services opportunity with YO IT Consulting for qualified professionals based anywhere in the world.

The company is hiring an AI Engineer to design production-grade machine learning models, automate deployment pipelines, and contribute expert technical input used to improve next-generation artificial intelligence systems.

The successful candidate will combine machine learning engineering, cloud infrastructure, software development, and container orchestration expertise.

The role involves translating complex real-world data challenges into scalable AI solutions using Python or Java, AWS, Kubernetes, and modern continuous integration and continuous delivery practices.

Job Details Country:
United Arab Emirates City:
Abu Dhabi Emirate Industry: IT and Services Function:
Software Engineering / Development Salary:  Estimated salary range based on similar jobs in the job city; please confirm the final offer with the employer.

Gender: Any Candidate Nationality:
Any Candidate Preferred

Location:

Any Job Type: Contract
- Remote - work from home.

Role Context The AI Engineer will work remotely on technical projects that help train, evaluate, and improve advanced artificial intelligence systems. Although previous experience in AI training is not required, candidates must possess strong professional knowledge of machine learning engineering, software development, model deployment, and cloud-based infrastructure. The role will focus on designing robust machine learning solutions, preparing real-world data problems for model development, and creating reliable production workflows.

The engineer will collaborate with data scientists, researchers, and software professionals while documenting technical decisions with accuracy and clarity.

• Design, develop, test, and optimize machine learning models for production environments.
• Translate complex business and data challenges into practical machine learning solutions.
• Evaluate real-world data problems and determine suitable modeling approaches.
• Frame machine learning objectives, assumptions, constraints, and success criteria.
• Prepare, clean, transform, and validate datasets for model development and evaluation.
• Select suitable machine learning algorithms based on business requirements and data characteristics.
• Train models and evaluate their accuracy, reliability, scalability, and operational suitability.
• Improve model performance through feature engineering, parameter tuning, validation, and error analysis.
• Build production-ready machine learning services that integrate with existing software systems.
• Develop automated end-to-end machine learning pipelines.
• Implement continuous integration and continuous delivery workflows for model testing and deployment.
• Automate model validation, packaging, release, monitoring, and rollback processes.
• Apply software engineering standards to machine learning codebases and supporting services.
• Develop maintainable applications using Python, Java, or other suitable programming languages.
• Work with large-scale software systems and distributed technical environments.
• Use AWS cloud services to build scalable machine learning infrastructure.
• Deploy models, applications, data pipelines, and supporting services within AWS environments.
• Configure cloud resources for performance, availability, security, and cost efficiency.
• Orchestrate containerized machine learning workloads using Kubernetes.
• Configure scalable deployments, services, jobs, resource controls, and availability mechanisms.
• Support reliable model execution across development, testing, and production environments.
• Monitor production machine learning services and investigate performance or reliability problems.
• Collaborate with data scientists to convert experimental models into dependable production solutions.
• Work with engineers and researchers to evaluate technical approaches and implementation trade-offs.
• Participate in technical reviews and provide practical recommendations based on engineering evidence.
• Identify opportunities to simplify machine learning workflows and improve deployment speed.
• Develop reusable tools, components, templates, and automation mechanisms.
• Document model architecture, infrastructure design, deployment processes, and technical decisions.
• Explain complex technical findings clearly to both specialist and non-specialist stakeholders.
• Maintain accurate records of experiments, assumptions, implementation choices, and evaluation results.
• Review generated AI outputs and provide high-quality technical feedback where required.
• Contribute…