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Senior Machine Learning Engineer

Job in Abu Dhabi, UAE/Dubai
Listing for: cander
Full Time position
Listed on 2026-08-13
Job specializations:
  • IT/Tech
    Machine Learning/ ML Engineer, AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 250000 - 380000 AED Yearly AED 250000.00 380000.00 YEAR
Job Description & How to Apply Below

This is a company focused on developing AI-driven solutions for defense and infrastructure, specializing in cutting-edge machine learning models for supply chain optimization, compliance automation, and predictive analytics while adhering to defense-grade security standards. Its transformative platforms combine generative AI with operational workflows to enhance efficiency, risk management, and decision-making in high-stakes industries.

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…
Position Requirements
10+ Years work experience
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