Machine Learning Engineer
Summary
Build and deploy production-grade ML systems, including LLMs and GenAI, from experimentation to live inference while integrating with core product features.
We have partnered with a high-growth technology business in the UAE to hire a Machine Learning Engineer. This is a hands-on role for someone who builds and ships ML systems that work in the real world, not just in notebooks. The business is at a stage where ML is becoming central to how the product operates and how decisions get made, and this person will be at the heart of that.
This role suits an engineer with a strong applied ML background who wants to work in a team that values technical rigour, moves fast and puts real ML into production.
About the role:
- Design, build and deploy machine learning models and systems into production, owning delivery from experimentation through to live inference
- Develop and maintain ML pipelines covering data preparation, feature engineering, model training, evaluation and deployment
- Work closely with data engineers and product teams to integrate ML models into core platform features and business workflows
- Implement MLOps practices including model versioning, monitoring, retraining pipelines and automated evaluation frameworks
- Apply LLM and GenAI techniques where appropriate, including fine-tuning, prompt engineering and retrieval-augmented generation
- Evaluate model performance, reliability, latency and cost in live environments, making trade-offs that balance accuracy with operational efficiency
About you:
- 5 years of hands‑on experience in machine learning engineering or applied ML, with a clear track record of deploying models into production
- Strong Python skills and deep familiarity with ML frameworks including TensorFlow, PyTorch or Scikit-learn
- Solid understanding of the full ML lifecycle including feature engineering, model evaluation, deployment and monitoring
- Experience with MLOps tooling and practices including experiment tracking, model registries and CI/CD for ML systems
- Familiarity with cloud platforms such as AWS, Azure or GCP and how ML systems are deployed and maintained at scale
- Exposure to LLMs, GenAI or NLP in a production environment is a strong advantage
