MLOps Engineer – 6 months contract

Position summary

Location

• Job location is Dubai, UAE; no explicit workplace arrangement is stated.

Salary and contract duration

• Salary is AED 15,000 per month.
• Contract duration is six months, with potential extension.

Responsibilities

• Deploy ML models and AI pipelines from proof of concept or development into production, scaling them efficiently and maintaining performance through CI/CD integration and orchestration.
• Implement monitoring and maintenance strategies for deployed models to sustain accuracy and reliability.
• Optimize models for inference speed and resource efficiency using quantization, pruning, and knowledge distillation for edge and cloud deployment.
• Collect and clean data and perform feature engineering to prepare training datasets.
• Implement continuous or semi-continuous training and evaluation workflows and fine-tune models to maintain accuracy and optimize performance.
• Collaborate with data scientists, software engineers, DevOps, and product managers to understand requirements and deliver ML solutions.
• Maintain clear, organized documentation of code, models, and processes.

Qualifications

• Bachelor's degree in Computer Science, Machine Learning, Data Science, AI, or a related field.
• Proficiency in Python and libraries such as PyTorch, NumPy, Pandas, and Scikit-learn.
• Knowledge of model deployment, containerisation, 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.
• Contributions to open-source computer-vision projects or relevant publications.

Core technical skills

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