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.