Machine Learning Engineer

Connect Tech+Talent; Dubai; On-site; AED 150,000 - 230,000; Full time

Job summary

Connect Tech+Talent is seeking an ML Engineer (MLOps) in Dubai to take machine-learning models from PoC to production across edge and cloud environments. You will own deployment, monitoring, and optimization, collaborating with data scientists, software engineers, DevOps, and product managers.

The role requires deep learning expertise, hands-on experience with PyTorch, Docker, Kubernetes, and cloud platforms, and a strong track record deploying scalable ML pipelines in production.

Qualifications

• Bachelor's or master's in CS/ML/DS/AI or related field.
• 5–9 years of relevant experience.
• Proficiency in Python and libraries (PyTorch, NumPy, Pandas, Scikit-learn).
• Experience with Docker and Kubernetes for deployment.
• Strong SQL and NoSQL database knowledge.
• Cloud experience (AWS, GCP, or Azure).
• Familiarity with MLflow, ClearML, Azure ML or SageMaker.
• Strong understanding of deep learning and reinforcement learning.

Responsibilities

• Deployment: Deploy ML models and AI pipelines from PoC to production with scalable CI/CD.
• Monitoring & Maintenance: Implement monitoring for ongoing accuracy and reliability.
• Model Optimisation & Pruning: Optimise for edge and cloud deployment.
• Data Preprocessing: Collect, clean and engineer features for training.
• Model Training & Tuning: Implement continuous training and evaluation workflows.
• Collaboration: Work with data scientists, software engineers, DevOps, and PMs.
• Documentation: Maintain clear documentation of code, models, and processes.

Skills

Python

PyTorch

NumPy

Pandas

Scikit-learn

Deep learning

Reinforcement learning

ML deployment

Education

Bachelor's or Master's degree in Computer Science, ML, DS, AI or related field

Tools

Docker

Kubernetes

SQL

NoSQL

AWS

GCP

Azure

MLflow

ClearML

Azure ML

SageMaker

TensorFlow

OpenCV

Job description

ML engineer

Location: Dubai

Duration: 12 months

Visa : Work Permit, Dependent, Tourist Visa.

Need Someone in Dubai only.

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 optimization across both edge and cloud environments.

Responsibilities

• Deployment: 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.
• Monitoring & Maintenance: Implement monitoring and maintenance strategies for deployed models to ensure ongoing accuracy and reliability.
• Model Optimisation & Pruning: Optimise models for inference speed and resource efficiency using techniques such as quantisation, pruning, and knowledge distillation for edge and cloud deployment.
• Data Preprocessing: Perform data collection, cleaning, and feature engineering to prepare datasets for training.
• Model Training & Tuning: Implement continuous / semi-continuous training and evaluation workflows to maintain accuracy over time, and fine-tune models for optimal performance.
• Collaboration: Work with data scientists, software engineers, DevOps, and product managers to understand requirements and deliver ML solutions.
• Documentation: Maintain clear, organised documentation of code, models, and processes.

Qualifications

• Bachelors or Masters degree in Computer Science, Machine Learning, Data Science, AI, or a related field.
• 5 – 9 years of relevant experience.
• 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.
• Frameworks & Libraries: PyTorch, TensorFlow, OpenCV, Scikit-learn, Pandas, NumPy, FastAPI.
• Serving ; Deployment: Docker, Kubernetes, GitLab CI (CI/CD).
• Databases: PostgreSQL, MySQL, MongoDB, Elasticsearch, Neo4j.
• Deep-Learning Architectures: CNN, LSTM, GAN, Transformers, LLM.
• MLOps & Distributed Computing: MLflow, Kubeflow, Ray, ClearML.
• Message Brokers & GPU: RabbitMQ, Kafka; CUDA, RAPIDS, Numba.
• Cloud Platforms: AWS, Azure, GCP.