Job Description
ML engineerLocation: Dubai, UAE
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.