Machine Learning Engineer

Location: Dubai

Duration: 6 months with extension

Salary : 15000 AED

 5-9 Years

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

•           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

•           Bachelor's or Master's 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.

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