AI Engineer

AI Engineer - MLOps (Job Snapshot)

Role: AI Engineer; Location: Dubai, United Arab Emirates; Industry: IT and Services; Function: IT-Software Development; Experience: 4 to 8 years; Job Type: Full-time; Salary: 26000-40000. Estimated salary range based on similar jobs in the job city; please confirm the final offer with the employer.

AI Engineer in Dubai, United Arab Emirates is an advanced IT and Services role focused on production-grade AI systems, MLOps frameworks, generative AI integration, scalable model deployment, and secure automation platforms for Majid Al Futtaim Holding. This opportunity is suited to a hands‑on AI and software engineering professional who can connect machine learning experimentation with reliable enterprise systems, reusable deployment pipelines, and business‑ready AI solutions.

Key Responsibilities

• Design and build end‑to‑end AI and machine learning solutions covering model training pipelines, deployment environments, monitoring, and performance optimization
• Develop APIs, microservices, and integration layers that embed AI models into business systems, data platforms, enterprise applications, and digital products
• Create and operate MLOps frameworks for model lifecycle management, including CI/CD for machine learning, automated testing, deployment orchestration, and release control
• Establish model governance practices covering versioning, documentation, approval flows, rollback procedures, auditability, and responsible AI controls
• Work with infrastructure, cloud, security, and DevOps teams to deliver secure, compliant, scalable, and cost‑optimized environments for AI workloads
• Integrate AI solutions with feature stores, vector databases, data lakes, enterprise data sources, and reusable data pipelines
• Build retrieval‑augmented generation solutions using embeddings, vector search, and large language models for enterprise knowledge and automation use cases
• Support AI delivery squads by designing shared components, standardized frameworks, deployment templates, and reusable engineering patterns
• Improve model performance through inference optimization techniques such as quantization, model compression, distillation, and efficient serving design
• Maintain strong reliability across deployed AI systems by supporting monitoring, troubleshooting, incident response, and continuous technical improvement

Ideal Profile

• Bachelor's or master's degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or a related field
• 4 to 8 years of professional experience in software engineering, data engineering, AI engineering, machine learning engineering, or platform development
• At least 3 years of focused experience in AI/ML engineering, MLOps, production model deployment, or enterprise machine learning infrastructure
• Proven experience delivering production‑grade AI systems and integrating models into real business applications or large‑scale digital platforms
• Hands‑on knowledge of AI and machine learning frameworks such as TensorFlow, PyTorch, scikit‑learn, or similar tools
• Practical experience with MLOps and orchestration tools such as MLflow, Kubeflow, Airflow, or related deployment and workflow platforms
• Strong software engineering ability with Python, API development, microservices, automated testing, and production‑grade coding practices
• Experience with model serving, inference optimization, vector databases, feature stores, data lakes, embeddings, and RAG‑based enterprise AI solutions
• Familiarity with DevOps practices, infrastructure as code, Terraform, Helm, containers, Kubernetes, and security standards for AI environments is beneficial
• Strong communication skills with the ability to work across technology, data, infrastructure, security, and business teams

Skills Set

• AI engineering
• Machine learning engineering
• MLOps
• Generative AI
• Production AI deployment
• Python
• API development
• Microservices
• TensorFlow
• PyTorch
• scikit‑learn
• MLflow
• Kubeflow
• Airflow
• CI/CD for machine learning
• Model lifecycle management
• Model governance
• Retrieval‑augmented generation
• Embeddings
• Vector databases
• Feature stores
• Data lakes
• Quantization
• Model compression
• Distillation
• Terraform
• Helm
• Kubernetes
• Secure AI platforms

Why Join Us

• Work on enterprise AI platforms within a leading UAE‑based group with strong investment in digital transformation and intelligent automation
• Build and deploy scalable AI systems that support data‑driven decisions, operational efficiency, and business innovation across multiple sectors