Machine Learning Engineer

Position summary

Responsibilities

• Deploy models and AI pipelines from proof of concept or development into production, scaling efficiently and integrating CI/CD and orchestration.
• Implement monitoring and maintenance strategies for ongoing model accuracy and reliability.
• Optimize inference speed and resource use through quantization, pruning, and knowledge distillation for edge and cloud.
• Collect and clean data and perform feature engineering for training datasets.
• Implement continuous or semi-continuous training and evaluation workflows and fine-tune models.
• Work with data scientists, software engineers, DevOps, and product managers to deliver ML solutions.
• Maintain organized 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, containerization, and orchestration, including Docker and 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 machine-learning techniques.

Preferred qualifications

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

Core technical skills

• Languages: Python.
• Frameworks and libraries listed: PyTorch, TensorFlow, OpenCV, Scikit-learn, Pandas, NumPy, and FastAPI.
• Serving and deployment tools listed: Docker, Kubernetes, and GitLab CI (CI/CD).
• Databases listed: PostgreSQL, MySQL, MongoDB, Elasticsearch, and Neo4j.
• Deep-learning architectures listed: CNN, LSTM, GAN, Transformers, and LLM.
• MLOps and distributed computing tools listed: MLflow, Kubeflow, Ray, and ClearML.
• Message brokers and GPU tools listed: RabbitMQ, Kafka, CUDA, RAPIDS, and Numba.
• Cloud platforms listed: AWS, Azure, and GCP.

Location

• The posting says the candidate must be in Dubai.