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LLM & NLP Pipelines\n 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.\n\n Rule Formalization: Convert natural language requirements into computer-processable formats (e.g. logic tuples) that can be executed by downstream compliance engines.\n\n Semantic Search: Implement RAG (Retrieval-Augmented Generation) architectures to enable semantic querying of technical documentation and historical project data.\n\n Prompt Engineering: optimize prompt strategies (few-shot learning chain-of-thought) to improve model performance on domain-specific tasks without extensive retraining.\n\n 2. Predictive & Analytical Models (Supply Chain)\n Forecasting Engines: Develop time-series forecasting models to predict material demand and spend categories integrating internal ERP data with external market signals.\n\n Risk Scoring: Build classification and anomaly detection models to assess supplier risk profiles based on financial health delivery performance and geopolitical factors.\n\n Optimization Algorithms: Design algorithms for multi-objective optimization (e.g. balancing cost vs. lead time vs. risk) to support procurement decision-making.\n\n 3. 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