AI Specialist & Machine Learning Engineer Jobs in Abu Dhabi & Dubai 2026 – G42 Group
Job Summary
G42 Healthcare & Technology, a leading artificial intelligence and cloud computing powerhouse in the UAE, is actively hiring an experienced AI Specialist & Machine Learning Engineer. The ideal candidate will architect, design, train, and deploy advanced predictive AI models and Large Language Models (LLMs) for large-scale enterprise and healthcare applications across Abu Dhabi and Dubai. This role plays a critical part in scaling cutting-edge generative AI, MLOps infrastructure, and intelligent automation solutions across regional and global markets.
Key Responsibilities
- Design, develop, and fine-tune machine learning models, deep learning architectures, and Large Language Models (LLMs) to address complex enterprise and healthcare challenges.
- Build robust MLOps pipelines for automated model training, validation, CI/CD deployment, monitoring, and continuous retraining at scale.
- Process, clean, and analyze complex structured and unstructured datasets, implementing advanced feature engineering and data transformation strategies.
- Collaborate with cross-functional teams, including software engineers, data architects, and domain experts, to integrate AI capabilities into production applications.
- Ensure strict compliance with data privacy regulations, AI ethics guidelines, and security standards established across G42 and UAE authorities.
Job Overview
| Employer | G42 (Group 42 Healthcare & Technology) |
|---|---|
| Location | Abu Dhabi & Dubai, United Arab Emirates |
| Monthly Remuneration | AED 22,000 – AED 40,000 per month (Tax-Free, negotiable based on senior expertise) |
| Education Requirement | Bachelor’s, Master’s, or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or Data Science |
| Technical Stack | Python, PyTorch, TensorFlow, LLMs (LangChain/LlamaIndex), MLOps, Docker, Kubernetes, AWS/Azure/G42 Cloud |
| Experience Level | Minimum 3-6 years of hands-on experience developing and deploying machine learning models in production |
| Key Competencies | Generative AI, Model Fine-Tuning (PEFT/LoRA), Natural Language Processing (NLP), MLOps, Deep Learning Algorithms |
| Benefits Package | Housing Allowance, Comprehensive Health Insurance, Annual Flight Tickets, Paid Leave, Visa Sponsorship, End-of-Service Gratuity |
Career Preparation Guide & Candidate Advice
How to Optimize Your AI & ML Resume for G42
- Highlight Production Deployment: Emphasize models you have successfully shipped to production rather than just academic or experimental projects.
- Detail Generative AI & LLM Experience: Specify frameworks (e.g., PyTorch, Transformers, LangChain) and techniques (fine-tuning, RAG, Quantization, LoRA) you have utilized in real-world environments.
- Showcase MLOps Capabilities: Mention hands-on tools for orchestration, containerization, and model monitoring (Docker, Kubernetes, MLflow, Kubeflow).
- Keep CV ATS-Friendly: Use standard technical headings (Professional Summary, Core Technical Skills, Production Experience, Key Projects, Education) for seamless recruitment parsing.
Common AI & ML Engineer Interview Questions & Recommended Answers
- Question: How do you address hallucination and latency challenges when deploying Large Language Models (LLMs) for enterprise production?
Tip: Explain strategies such as Retrieval-Augmented Generation (RAG) with vector databases, prompt guardrails, fine-tuning, model quantization (vLLM/TensorRT-LLM), and efficient caching mechanisms. - Question: How do you handle severe class imbalance or noisy data in complex machine learning datasets?
Tip: Discuss technique selection such as SMOTE, focal loss, re-weighting loss functions, robust data cleaning pipelines, and evaluating with precision-recall (PR-AUC) metrics instead of raw accuracy.
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