Senior Machine Learning Engineer (UAE)
Please mention DailyRemote when applying
AI Summary
The role involves designing and fine-tuning LLM and NLP pipelines for regulatory parsing and rule formalization, alongside developing predictive models for supply chain optimization. Additionally, the engineer will manage MLOps tasks, including model deployment, pipeline orchestration, and performance monitoring.
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.
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