Senior Machine Learning Engineer, Abu Dhabi, UAE

This job is OPEN to APPLY for ALL Nationalities, unless otherwise specified.


Job Summary
This company, is seeking a Senior Machine Learning Engineer to spearhead the development and implementation of cutting-edge AI solutions for high-impact initiatives. In this pivotal role, you will oversee the complete lifecycle of machine learning systems—from conceptual design and data preparation to model training, optimization, and secure deployment in production environments. You will drive innovation at the intersection of generative AI and traditional machine learning, focusing on two transformative projects: one being, an automated requirements engineering platform leveraging large language models (LLMs) for regulatory compliance and rule extraction, and the Intelligent Supply Chain, which integrates predictive analytics for demand forecasting, risk assessment, and procurement optimization. Operating within a structured agile framework—from Sprint Zero to Stage Gate—you will ensure that all models meet rigorous standards for accuracy, robustness, explainability, and defense-grade security. This role demands a blend of technical expertise, domain adaptability, and collaborative leadership. You will work closely with cross-functional teams, including data scientists, backend engineers, and domain experts, to align technical solutions with business objectives while adhering to strict engineering best practices and documentation requirements.

Key Responsibilities
Lead the design and implementation of Large Language Model (LLM) pipelines, including parsing complex regulatory texts (e.g., military standards, building codes) to extract structured rules and formalize natural language requirements into executable logic tuples.

Develop and optimize Retrieval-Augmented Generation (RAG) architectures to enable semantic search and querying of technical documentation and historical project data for compliance and decision-making purposes.

Engineer advanced prompt strategies (e.g., few-shot learning, chain-of-thought) to enhance model performance on domain-specific tasks while minimizing the need for extensive retraining.

Build time-series forecasting models to predict material demand and spend categories, integrating internal ERP data with external market signals for supply chain optimization.

Design classification and anomaly detection models to assess supplier risk profiles based on financial health, delivery performance, and geopolitical factors.

Create multi-objective optimization algorithms to balance conflicting priorities such as cost, lead time, and risk in procurement decision-making processes.

Containerize machine learning models using Docker and Kubernetes, deploying them into secure, on-premise inference environments adhering to defense-grade security standards.

Construct automated training and inference pipelines with tools like Kubeflow or MLflow to ensure reproducibility, scalability, and compliance with engineering best practices.

Optimize model inference latency and resource efficiency through techniques such as quantization and distillation, ensuring seamless operation on available hardware.

Implement comprehensive monitoring systems to track model drift and performance degradation in production, establishing feedback loops for continuous improvement and retraining.

Professional Qualifications
Five or more years of experience in Machine Learning Engineering, with a proven track record of deploying models into production environments.

Ability to quickly learn and apply machine learning techniques to specialized domains such as defense engineering, supply chain, or construction.

Experience working in agile environments using Sprints while adhering to rigorous engineering standards and documentation requirements.

Strong communication skills to collaborate effectively with Data Scientists, Backend Engineers, and Domain Experts, ensuring technical solutions align with business needs.

Required Technical Skills
Expert proficiency in Python and standard machine learning libraries including PyTorch, TensorFlow, Scikit-learn, Pandas, and NumPy.

Strong experience with transformer architectures (BERT, GPT, Llama) and natural language processing frameworks such as Hugging Face and LangChain.

Proficiency in MLOps tools and practices, including containerization (Docker), orchestration (Kubernetes), and experiment tracking (MLflow).

Ability to design and implement data preprocessing pipelines for both structured data (SQL, tabular formats) and unstructured data (text, PDFs).

Deep understanding of algorithmic principles for custom logic implementation, including graph traversal and geometric computations.

Experience with Retrieval-Augmented Generation (RAG) architectures and advanced prompt engineering techniques (few-shot learning, chain-of-thought).

Familiarity with time-series forecasting models, classification algorithms, and anomaly detection techniques for predictive analytics.

Knowledge of model optimization strategies such as quantization, distillation, and latency reduction for production deployment.

Hands-on experience with model deployment frameworks, including containerization and secure on-premise inference environments.

Ability to build and maintain automated training and inference pipelines to ensure reproducibility and scalability in production.



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