Machine Learning Engineer

DISCOVERED; Abu Dhabi; On-site; AED 250,000 - 420,000; Full time

Job summary

DISCOVERED is establishing an AI Centre of Excellence in Abu Dhabi, driving AI from pilot projects to scaled, production-ready solutions across core functions. The role focuses on building scalable ML systems and pipelines to enable real-world business impact.

The Machine Learning Engineer will own the full ML lifecycle—from data handling and model training to deployment and optimisation in production, including fine-tuning LLMs/SLMs, model serving APIs, and integrating models with broader

Qualifications

• 3-7 years' experience building scalable, production-grade AI or ML systems.
• Strong experience across supervised and unsupervised learning, deep learning, NLP, CV or generative AI.
• Strong ML systems architecture knowledge.
• Hands-on experience fine-tuning and deploying LLMs or SLMs.
• Experience with model serving and API development using FastAPI or Flask.
• Good understanding of Docker, Kubernetes, CI/CD and MLOps tooling such as MLflow or Kubeflow.
• Experience deploying ML workloads on AWS or Azure.
• Bachelor's degree in Computer Science, Engineering or a related field.

Responsibilities

• Design, build and deploy scalable ML systems for business problems.
• Cover the full ML lifecycle from data handling to deployment and optimisation.
• Collaborate with DS, Engineering and Product teams to integrate models into applications.
• Fine-tune and deploy LLMs/SLMs and build model-serving APIs; improve real-time and batch inference.

Skills

ML systems architecture

Model deployment

API development

MLOps tooling

Cloud deployment (AWS/Azure)

Education

Bachelor's degree in Computer Science or Engineering

Master's degree or PhD in CS/AI (plus)

Tools

Docker

Kubernetes

CI/CD

MLflow

Kubeflow

FastAPI

Flask

AWS

Azure

Job description

Job description

This is a chance to join a multinational organisation building an AI Centre of Excellence in Abu Dhabi, with a clear focus on moving AI from experimentation into scaled deployment across the group. The business is investing heavily in AI and advanced analytics, taking promising pilots and turning them into practical, production-ready solutions across core functions. They're now looking for a Machine Learning Engineer to help build the systems, models and pipelines that make that possible.

The role

You’ll design, build and deploy scalable machine learning systems that solve real business problems and support better decision-making across the organisation. The work will span the full ML lifecycle, from working with large and complex datasets through to model training, validation, deployment and optimisation in production. You'll work across traditional machine learning, neural networks, generative AI and agentic systems, depending on the use case. You'll also build end-to-end ML pipelines, automate training and deployment workflows, and work closely with Data Science, Engineering and Product teams to integrate models into wider applications. A big part of the role will be making sure models work beyond the prototype stage, whether that means fine-tuning SLMs and LLMs, building model-serving APIs or improving performance across real-time and batch inference environments.

What we're looking for

• 3-7 years' experience building scalable, production-grade AI or machine learning systems.
• Strong experience across supervised and unsupervised learning, deep learning, NLP, computer vision or generative AI.
• Strong ML systems architecture knowledge.
• Hands-on experience fine-tuning and deploying LLMs or SLMs.
• Experience with model serving and API development using tools such as FastAPI or Flask.
• Good understanding of Docker, Kubernetes, CI/CD and MLOps tooling such as MLflow or Kubeflow.
• Experience deploying machine learning workloads on AWS or Azure.
• Bachelor's degree in Computer Science, Engineering or a related field.

A Master's degree or PhD in Computer Science, AI, Machine Learning or a related field would be a plus.