This is a remote position.

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.

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.

Employment Type : Full Time
Experience: years
Vacancy: 1

Senior Machine Learning Engineer (UAE)

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