Senior Machine Learning Engineer (UAE)
Actively Hiring
Full-time Posted about 2 months ago
Responsibilities
- check_circle Design and fine-tune Large Language Model (LLM) pipelines to interpret complex regulatory texts (e.g., military standards, building codes) and extract structured rules.
- check_circle Convert natural language requirements into computer-processable formats (e.g., logic tuples) that can be executed by downstream compliance engines.
- check_circle Implement RAG (Retrieval-Augmented Generation) architectures to enable semantic querying of technical documentation and historical project data.
- check_circle Optimize prompt strategies (few-shot learning, chain-of-thought) to improve model performance on domain-specific tasks without extensive retraining.
- check_circle Develop time-series forecasting models to predict material demand and spend categories, integrating internal ERP data with external market signals.
- check_circle Build classification and anomaly detection models to assess supplier risk profiles based on financial health, delivery performance, and geopolitical factors.
- check_circle Design algorithms for multi-objective optimization (e.g., balancing cost vs. lead time vs. risk) to support procurement decision-making.
- check_circle Containerize models using Docker/Kubernetes and deploy them into secure, on-premise inference environments.
- check_circle Build automated training and inference pipelines using tools like Kubeflow or MLflow to ensure reproducibility and scalability.
- check_circle Optimize model inference latency and resource usage (e.g., quantization, distillation) to run efficiently on available hardware.
- check_circle Implement monitoring systems to track model drift and performance in production, establishing feedback loops for continuous improvement.
Basic qualifications
- Expert proficiency in Python and standard ML libraries (PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy).
- Strong experience with transformer architectures (BERT, GPT, Llama) and NLP frameworks (Hugging Face, LangChain).
- Proficiency with MLOps tools and practices, including containerization (Docker), orchestration (Kubernetes), and experiment tracking (MLflow).
- Ability to design data preprocessing pipelines for both structured (SQL, tabular) and unstructured (text, PDF) data.
- Strong grasp of algorithmic principles for implementing custom logic, such as graph traversal or geometric computations.
Tags & Focus Areas
Remote Machine Learning Nlp Ai