MLOps Engineer – 6 months contract

Dashco Middle East; Dubai; On-site; AED 152,000 - 182,000; Full time

Job summary

Dashco Middle East, Dubai, UAE, seeks a Machine Learning Engineer (MLOps) to bring ML models from PoC to scalable production. You will own deployment, monitoring, and optimisation across edge and cloud environments, collaborating with data scientists, software engineers, DevOps, and product managers.

The role requires strong Python and ML tooling experience, plus familiarity with Docker, Kubernetes, and cloud platforms.

Qualifications

• Bachelor's degree in Computer Science, ML, Data Science, AI, or related field

Responsibilities

• Deploy ML models and AI pipelines from PoC to production with CI/CD integration
• Implement monitoring and maintenance strategies for deployed models
• Optimise models for inference speed and resource efficiency for edge and cloud deployment
• Perform data collection, cleaning and feature engineering
• Implement continuous / semi-continuous training and evaluation workflows
• Collaborate with data scientists, software engineers, DevOps and product managers
• Maintain clear, organised documentation of code, models, and processes

Education

Bachelor's degree in Computer Science, Machine Learning, Data Science, AI, or a related field

Tools

Python

PyTorch

NumPy

Pandas

Scikit-learn

Docker

Kubernetes

SQL

NoSQL

AWS

GCP

Azure

MLflow

ClearML

Azure ML

AWS SageMaker

Job description

Hiring: Machine Learning Engineer (MLOps) | Dubai, UAE

Job Location: Dubai, UAE

Contract Duration: 6 months, with potential extension

Salary: AED 15,000 per month

Position Summary:

As an ML Engineer (MLOps), you will take machine-learning models and AI pipelines from proof-of-concept through to scalable, reliable production deployment. You will own deployment, monitoring, and optimisation across both edge and cloud environments.

Responsibilities:

• Deploy ML models and AI pipelines from PoC / development to production, ensuring they scale efficiently and maintain high performance through seamless CI/CD integration and orchestration.
• Implement monitoring and maintenance strategies for deployed models to ensure ongoing accuracy and reliability.
• Optimise models for inference speed and resource efficiency using techniques such as quantisation, pruning, and knowledge distillation for edge and cloud deployment.
• Perform data collection, cleaning, and feature engineering to prepare datasets for training.
• Implement continuous / semi-continuous training and evaluation workflows to maintain accuracy over time, and fine-tune models for optimal performance.
• Work with data scientists, software engineers, DevOps, and product managers to understand requirements and deliver ML solutions.
• Maintain clear, organised documentation of code, models, and processes.

Qualifications:

• Bachelor's degree in Computer Science, Machine Learning, Data Science, AI, or a related field.
• Proficiency in Python and libraries such as PyTorch, NumPy, Pandas, and Scikit-learn.
• Knowledge of model deployment, containerisation, and orchestration (Docker, Kubernetes).
• Knowledge of SQL and NoSQL databases.
• Familiarity with one or more cloud platforms (AWS, GCP, or Azure).
• Familiarity with MLOps tools such as MLflow, ClearML, Azure ML, or AWS SageMaker.
• Strong understanding of deep learning, reinforcement learning, and other ML techniques.

Preferred Qualifications:

• Experience deploying computer-vision models to edge devices or low-resource environments.
• Familiarity with infrastructure-as-code tools and observability platforms.
• Contributions to open-source computer-vision projects or relevant publications.

Core Technical Skills

• Languages: Python.