Senior Machine Learning Engineer

Salt; Abu Dhabi Emirate; On-site; AED 260,000 - 520,000; Full time

Job summary

Salt is embarking on a brand-new project: an Innovation Hub/Ai Lab backed by a regional bank. We seek a production-focused Machine Learning Engineer with 3-7 years of hands-on experience to build scalable AI/ML systems, deploy models, and optimize performance.

The ideal candidate demonstrates deep Python expertise, proficiency in ML engineering, and hands-on experience with model serving via FastAPI/Flask, containerization with Docker and Kubernetes, and cloud deployment on AWS or Azure.

Qualifications

• 3-7 years of building production-grade, scalable AI/ML systems.
• Strong Python and ML engineering capabilities.
• Experience with supervised/unsupervised ML, deep learning, NLP and/or GenAI/LLMs.

Skills

Python

ML engineering

Production ML systems

Deep learning

NLP

Computer Vision

GenAI/LLMs

FastAPI/Flask

Docker

Kubernetes

CI/CD

MLflow

Kubeflow

AWS/Azure deployment

Real-time inference

Model performance optimization

Tools

Docker

Kubernetes

CI/CD

Job description

We are working on a brand-new project, which is an Innovation Hub/Ai Lab backed by a large bank in the region.

The candidate my client want is essentially a production-focused Machine Learning Engineer with 3-7 years' experience, rather than a pure Data Scientist. The major requirements I would source against are:

• 3-7 years' experience building production-grade, scalable AI/ML systems.
• Very strong Python + ML engineering capability.
• Experience with supervised/unsupervised ML, deep learning, NLP, Computer Vision and/or GenAI/LLMs - you don't necessarily need every area but should have genuine depth.
• Production model serving and APIs using FastAPI / Flask.
• Experience with real-time and/or batch inference and model performance optimisation.
• Knowledge of Docker, Kubernetes and CI/CD.
• Exposure to MLflow and/or Kubeflow for lifecycle management.
• Deployment onto AWS or Azure.
• Experience training/deploying traditional ML, neural networks and Agentic AI models.
• Ideally experience fine-tuning SLMs/LLMs and building more complex AI architectures.