Senior Machine Learning Engineer (UAE)

This is a remote position. * **Location:** Remote – UAE * **Requirement:** A Valid UAE work permit/employment visa is mandatory. * **Employment type:** Independent Contractor **Key Responsibilities** **1\. LLM \& NLP Pipelines** * **Regulation Parsing:** Design and fine\-tune Large Language Model (LLM) pipelines to interpret complex regulatory texts (e.g., military standards, building codes) and extract structured rules. * **Rule Formalization:** Convert natural language requirements into computer\-processable formats (e.g., logic tuples) that can be executed by downstream compliance engines. * **Semantic Search:** Implement RAG (Retrieval\-Augmented Generation) architectures to enable semantic querying of technical documentation and historical project data. * **Prompt Engineering:** optimize prompt strategies (few\-shot learning, chain\-of\-thought) to improve model performance on domain\-specific tasks without extensive retraining. **2\. Predictive \& Analytical Models (Supply Chain)** * **Forecasting Engines:** Develop time\-series forecasting models to predict material demand and spend categories, integrating internal ERP data with external market signals. * **Risk Scoring:** Build classification and anomaly detection models to assess supplier risk profiles based on financial health, delivery performance, and geopolitical factors. * **Optimization Algorithms:** Design algorithms for multi\-objective optimization (e.g., balancing cost vs. lead time vs. risk) to support procurement decision\-making. **3\. MLOps \& Productionization** * **Model Deployment:** Containerize models using Docker/Kubernetes and deploy them into secure, on\-premise inference environments. * **Pipeline Orchestration:** Build automated training and inference pipelines using tools like Kubeflow or MLflow to ensure reproducibility and scalability. * **Performance Optimization:** Optimize model inference latency and resource usage (e.g., quantization, distillation) to run efficiently on available hardware. * **Monitoring \& retraining:** Implement monitoring systems to track model drift and performance in production, establishing feedback loops for continuous improvement. ### **Requirements** * **Core ML/AI:** Expert proficiency in Python and standard ML libraries (PyTorch, TensorFlow, Scikit\-learn, Pandas, NumPy). * **NLP \& GenAI:** Strong experience with transformer architectures (BERT, GPT, Llama) and NLP frameworks (Hugging Face, LangChain). * **MLOps:** Proficiency with MLOps tools and practices, including containerization (Docker), orchestration (Kubernetes), and experiment tracking (MLflow). * **Data Handling:** Ability to design data preprocessing pipelines for both structured (SQL, tabular) and unstructured (text, PDF) data. * **Algorithm Design:** Strong grasp of algorithmic principles for implementing custom logic, such as graph traversal or geometric computations.