Machine Learning Engineer

    Involved Solutions

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    Machine Learning Engineer

    Involved Solutions
    Abu Dhabi, Abu Dhabi Emirate, United Arab Emirates Full-timePosted 10 Aug 2026
    Technology, Information and Internet
    Job Description

    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 inferenceDevelop and maintain ML pipelines covering data preparation, feature engineering, model training, evaluation and deploymentWork closely with data engineers and product teams to integrate ML models into core platform features and business workflowsImplement MLOps practices including model versioning, monitoring, retraining pipelines and automated evaluation frameworksApply LLM and GenAI techniques where appropriate, including fine-tuning, prompt engineering and retrieval-augmented generationEvaluate 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 productionStrong Python skills and deep familiarity with ML frameworks including TensorFlow, PyTorch or Scikit-learnSolid understanding of the full ML lifecycle including feature engineering, model evaluation, deployment and monitoringExperience with MLOps tooling and practices including experiment tracking, model registries and CI/CD for ML systemsFamiliarity with cloud platforms such as AWS, Azure or GCP and how ML systems are deployed and maintained at scaleExposure to LLMs, GenAI or NLP in a production environment is a strong advantage

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