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.

Originally posted on Himalayas