We have partnered with a high-growth technology business in the UAE to hire a Machine Learning Engineer in Abu Dhabi. This is a hands-on role for someone who builds and ships ML systems that work in the real world, not just in notebooks. ML is becoming central to how this product operates and how decisions get made — and this person will be at the heart of that.
About the Role — Production ML Engineering
Core Focus: Designing, building, and deploying ML models and systems into production
Ownership: Delivery from experimentation through to live inference
Emerging Tech: LLM and GenAI techniques including fine-tuning and RAG
Team Fit: Values technical rigour, moves fast, ships real ML into production
Job Type & Tech Stack Info
Job Type: Full-Time, Hands-On, Production-Focused
ML Frameworks: TensorFlow, PyTorch, Scikit-learn
Cloud Platforms: AWS, Azure, or GCP
MLOps Tools: Experiment tracking, model registries, CI/CD for ML
Position Overview
As Machine Learning Engineer, you will design, build, and deploy machine learning models and systems into production, owning delivery from experimentation through to live inference. You’ll develop and maintain ML pipelines covering data preparation, feature engineering, training, evaluation, and deployment, work closely with data engineers and product teams to integrate models into core platform features, implement MLOps practices, apply LLM and GenAI techniques where appropriate, and evaluate model performance, reliability, latency, and cost in live environments.
Why This Role Matters: As Machine Learning Engineer, you ship ML systems that directly power a high-growth product rather than staying in notebooks, gain hands-on exposure to LLM, GenAI, and RAG techniques in production, build deep MLOps expertise including model versioning, monitoring, and retraining pipelines, join a team that moves fast and values technical rigour, and position yourself at the center of a business where ML drives real decisions.
Key Responsibilities
Model Design & Production Deployment
- Design, build, and deploy machine learning models and systems into production
- Own delivery from experimentation through to live inference
- Balance accuracy with operational efficiency in live environments
ML Pipeline Development
- Develop and maintain ML pipelines covering data preparation and feature engineering
- Manage model training, evaluation, and deployment workflows
- Work with data engineers and product teams to integrate models into core features
MLOps & Monitoring
- Implement MLOps practices including model versioning and monitoring
- Build retraining pipelines and automated evaluation frameworks
- Evaluate model performance, reliability, latency, and cost
LLM & GenAI Application
- Apply LLM and GenAI techniques including fine-tuning and prompt engineering
- Implement retrieval-augmented generation (RAG) where appropriate
- Explore NLP applications relevant to core business workflows
Qualifications & Requirements
Experience Requirements
- 5 years of hands-on experience in machine learning engineering or applied ML
- Clear track record of deploying models into production
Technical Skills
- Strong Python skills and deep familiarity with TensorFlow, PyTorch, or Scikit-learn
- Solid understanding of the full ML lifecycle: feature engineering, evaluation, deployment, monitoring
- Experience with MLOps tooling including experiment tracking, model registries, and CI/CD for ML systems
- Familiarity with cloud platforms such as AWS, Azure, or GCP
Preferred Skills
- Exposure to LLMs, GenAI, or NLP in a production environment is a strong advantage
About the Opportunity
This Machine Learning Engineer role is with a high-growth technology business in Abu Dhabi, UAE, at a stage where ML is becoming central to how the product operates and how decisions get made. It’s a hands-on opportunity for engineers who want to build and ship real ML systems in production — not just experiment in notebooks — within a fast-moving team that values technical rigour.
Career Growth: Shape core ML infrastructure and GenAI capabilities for a fast-scaling UAE technology company.
Who Should Apply?
- Applied ML Engineers: With 5+ years shipping models into production
- MLOps Specialists: Experienced with CI/CD, model registries, and monitoring pipelines
- LLM/GenAI Practitioners: Skilled in fine-tuning, prompt engineering, and RAG
- Cloud ML Engineers: Comfortable deploying and scaling ML systems on AWS, Azure, or GCP
- Python Developers: Strong in TensorFlow, PyTorch, or Scikit-learn seeking a production-focused role
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