ML/AI Engineer — LLMs, MLOps & Production AI

SFORS; Dubai; On-site; AED 350,000 - 700,000; Full time

Benefits offered by this job

Housing support

Health insurance

Performance bonus

Professional training

Job summary

SFORS invites an ML/AI Engineer to join our UAE office to design, build, and deploy machine learning models and AI-powered applications at scale. You will productionize ML pipelines and integrate LLM/agent-based solutions to ensure reliability and performance.

You will collaborate with data engineering and analytics teams to source high-quality training data and embed AI capabilities into real-world systems, while upholding responsible AI practices and governance standards.

Qualifications

• Design, train, evaluate, and deploy ML models and AI/LLM-based solutions.
• Develop and maintain ML pipelines for training, evaluation, versioning, and serving (MLOps).
• Build agentic workflows and LLM-powered applications (RAG systems, tool-using agents).
• Monitor model performance in production and iterate to reduce drift and latency.
• Collaborate with data engineers to ensure ML-ready datasets and data requirements.

Responsibilities

• Design, train, evaluate, and deploy ML models and AI/LLM-based solutions into production
• Build and maintain ML pipelines for training, evaluation, versioning, and serving (MLOps)
• Develop and integrate agentic workflows and LLM-powered applications (e.g., RAG systems)
• Monitor model performance in production and iterate accordingly
• Collaborate with data engineers to define data requirements and ensure ML-ready datasets

Skills

Python

SQL

ML frameworks

MLOps

Education

Bachelor's in CS/Math/related

Tools

Docker

Kubernetes

MLflow

Vertex AI

Job description

SFORS invites an ML/AI Engineer to join our UAE office to design, build, and deploy machine learning models and AI-powered applications at scale. You will productionize ML pipelines and integrate LLM/agent-based solutions to ensure reliability and performance.

You will collaborate with data engineering and analytics teams to source high-quality training data and embed AI capabilities into real-world systems, while upholding responsible AI practices and governance standards.