This is a remote position.

Key Responsibilities

1. Generative AI & NLP for Engineering

Data Exploration and Analysis: Query and analyse large domain- or topic-specific data sets from both structured and unstructured sources identify patterns and features. Ensure data meets quality standards and requirements before model development.
Regulation Text Interpretation: Design and fine-tune Large Language Models (LLMs) to parse complex regulatory texts (e.g. building codes military standards) and extract structured rules for automated compliance checking.
Rule Formalization: Convert interpreted regulations into computer-processable formats (e.g. object-property-condition-value tuples) that can be executed by downstream compliance engines.
Querying via NLP: Architect methods for LLMs to map natural language requirements directly to specific metadata entities within various schemas (e.g. mapping systems design to specified attributes).
RAG Architecture: Implement Retrieval-Augmented Generation (RAG) pipelines that allow systems to query vast repositories of technical documentation and historical project data with high accuracy and low hallucination rates.

2. Predictive Modeling & Optimization (Supply Chain)

Forecasting Engines: Develop time-series forecasting models to predict spend categories and material demand by correlating internal ERP data with external macroeconomic signals.
Classification & Risk Scoring: Build machine learning classifiers to categorize supplier risks and operational anomalies integrating data from diverse sources to create dynamic risk scores.
Data Extraction Pipelines: Design robust pipelines to extract and transform raw data (from Data Lakehouse external web sources or SAP and other databases) into features required for predictive modeling and automated rule checking.

3. System Integration & Performance

Model Orchestration: Work with Back End Engineers to integrate AI models into a cohesive compliance engine or risk engine that can be invoked programmatically via robust APIs.
Optimization: Streamline model performance to ensure complex checks (e.g. analyzing large datasets or processing thousands of supplier records) can be executed within reasonable timeframes potentially using batching or asynchronous processing.
Quality Assurance: Validate model outputs against known test cases and historical data debugging false positives/negatives to refine algorithms and ensure defense-grade reliability.

Core AI/ML: Expert proficiency in Python and standard ML libraries (TensorFlow/PyTorch Scikit-learn Pandas NumPy). Strong grasp of both supervised and unsupervised learning techniques.
NLP & LLMs: Deep experience with transformer-based models (GPT BERT Llama) and prompt engineering techniques (few-shot learning fine-tuning) for domain-specific tasks.
Data Engineering: Proficiency in handling complex data structures (JSON XML) and familiarity with database querying (SQL/NoSQL) or graph data structures. Experience with data extraction from specialized formats is a significant plus.
Backend Awareness: Understanding of how to expose models via RESTful APIs (Flask/FastAPI) and integrate them into larger software architectures.
Statistics: Solid understanding of statistics probability distribution A/B testing. Adept at identifying and mitigating biases in datasets

Employment Type : Full Time
Experience: years
Vacancy: 1

Senior Data Scientist (UAE)

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