شرح موقعیت
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. Preferred Qualifications Core Technical Skills
مسئولیتها
- 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.
نیازمندیها
- 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.
- 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.
- 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.