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Senior Machine Learning Engineer

cander
Abu Dhabi, UAE
Full Time
Senior
Onsite
2 weeks ago
PythonPyTorchTensorFlowScikit learnPandasNumPy
Free

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About the Company

  • Headquartered in Abu Dhabi, UAE, specializes in developing AI driven solutions for defense engineering and supply chain optimization.
  • The company focuses on integrating generative AI and predictive analytics to automate complex requirements parsing and optimize procurement and logistics efficiencies within security constrained environments.

Job Summary

  • We are seeking a Senior Machine Learning Engineer to lead the development and deployment of cutting edge AI models for our Intelligent Supply Chain and platform initiatives.
  • You will oversee the entire lifecycle of machine learning systems—from architectural design and data preprocessing to model training, optimization, and secure production deployment.
  • Your work will bridge generative AI and traditional machine learning, driving innovation in two key areas: the platform (automated requirements engineering) and Intelligent Supply Chain (predictive risk scoring and demand forecasting).

Key Responsibilities

  • Lead the design and implementation of Large Language Model (LLM) pipelines for automated requirements engineering, focusing on parsing complex regulatory texts such as military standards and building codes to extract structured rules.
  • Convert natural language requirements into executable logic tuples and formalized formats for downstream compliance engines, ensuring seamless integration with technical workflows.
  • Develop Retrieval Augmented Generation (RAG) architectures to enable semantic search capabilities across technical documentation and historical project data, enhancing query precision and retrieval efficiency.
  • Optimize prompt engineering strategies, including few shot learning and chain of thought techniques, to improve model performance on domain specific tasks without requiring extensive retraining.
  • Design and deploy time series forecasting models to predict material demand and spend categories, integrating internal ERP data with external market signals for accurate supply chain planning.
  • Build classification and anomaly detection models to assess supplier risk profiles based on financial health, delivery performance, and geopolitical factors, ensuring robust risk mitigation strategies.
  • Create multi objective optimization algorithms to balance critical procurement factors such as cost, lead time, and risk, directly supporting data driven decision making in supply chain operations.
  • Containerize machine learning models using Docker and Kubernetes, deploying them into secure, on premise inference environments that meet defense grade security standards.
  • Construct automated training and inference pipelines using Kubeflow or MLflow to ensure reproducibility, scalability, and seamless integration with existing MLOps workflows.
  • Optimize model inference latency and resource usage through techniques such as quantization and distillation, ensuring efficient performance across available hardware configurations.
  • Implement comprehensive monitoring systems to track model drift and performance degradation in production, establishing feedback loops for continuous improvement and retraining.

Required Qualifications

  • 5+ years of experience in Machine Learning Engineering, with a proven track record of deploying models into production environments.
  • Expert proficiency in Python and standard ML libraries including PyTorch, TensorFlow, Scikit learn, Pandas, and NumPy.
  • Strong experience with transformer architectures (BERT, GPT, Llama) and NLP frameworks such as Hugging Face and LangChain.
  • Proficiency with MLOps tools and practices, including containerization (Docker), orchestration (Kubernetes), and experiment tracking (MLflow).
  • Ability to design data preprocessing pipelines for both structured (SQL, tabular) and unstructured (text, PDF) data.
  • Strong grasp of algorithmic principles for implementing custom logic, such as graph traversal or geometric computations.
  • Experience working in agile environments (Sprints) while adhering to rigorous engineering standards and documentation requirements.
  • Ability to quickly learn and apply ML techniques to specialized domains like defense engineering, supply chain, or construction.
  • Strong communication skills to collaborate effectively with Data Scientists, Backend Engineers, and Domain Experts, aligning technical solutions with business needs.

Technical Skills

  • Expert proficiency in Python and standard machine learning libraries including PyTorch, TensorFlow, Scikit learn, Pandas, and NumPy.
  • Strong experience with transformer architectures such as BERT, GPT, and Llama, and proficiency in NLP frameworks like Hugging Face and LangChain.
  • Deep understanding of MLOps tools and practices, including containerization with Docker, orchestration with Kubernetes, and experiment tracking with MLflow.
  • Ability to design and implement data preprocessing pipelines for both structured data (SQL, tabular formats) and unstructured data (text, PDFs).
  • Experience developing and optimizing retrieval augmented generation (RAG) architectures for semantic search and knowledge retrieval.
  • Proficiency in prompt engineering techniques, including few shot learning and chain of thought methodologies, to enhance model performance on domain specific tasks.
  • Strong grasp of algorithmic principles for custom logic implementation, including graph traversal, geometric computations, and multi objective optimization algorithms.
  • Experience deploying machine learning models into production environments using secure, on premise inference systems and optimizing inference latency through techniques such as quantization and distillation.
  • Familiarity with time series forecasting models and their integration with enterprise resource planning (ERP) systems and external market data.
  • Knowledge of classification and anomaly detection models for risk assessment, particularly in domains like financial health, delivery performance, and geopolitical risk scoring.
  • Experience building automated training and inference pipelines to ensure reproducibility, scalability, and compliance with rigorous engineering standards.
  • Ability to implement monitoring systems for tracking model drift and performance degradation in production environments.

Location and Project Focus

  • Location: Abu Dhabi, UAE
  • Project Focus: Intelligent Supply Chain & Platform

Role Overview

  • You will lead the development and deployment of advanced AI models for the client’s Intelligent Supply Chain and platform initiatives.
  • This role encompasses the full lifecycle of machine learning systems—from architectural design and data preprocessing to model training, optimization, and secure production deployment.
  • Your work will bridge generative AI and traditional machine learning, powering two core initiatives: an automated requirements engineering platform (leveraging LLMs) and an Intelligent Supply Chain system (focusing on predictive risk scoring and demand forecasting).

LLM & NLP Pipelines (Platform)

  • Design and refine Large Language Model pipelines to parse complex regulatory texts (e.g., military standards, building codes) and extract structured rules.
  • Convert natural language requirements into executable formats (e.g., logic tuples) for downstream compliance engines.
  • Implement Retrieval Augmented Generation (RAG) architectures to enable semantic search across technical documentation and historical project data.
  • Optimize prompt strategies—such as few shot learning and chain of thought—to enhance model performance on domain specific tasks without extensive retraining.

Predictive & Analytical Models (Supply Chain)

  • Develop time series forecasting models to predict material demand and spend, integrating ERP data with external market signals.
  • Build classification and anomaly detection models to assess supplier risk profiles based on financial health, delivery performance, and geopolitical factors.
  • Design multi objective optimization algorithms (e.g., balancing cost, lead time, and risk) to support procurement decision making.

MLOps & Productionization

  • Containerize models using Docker and Kubernetes, deploying them into secure on premise inference environments.
  • Construct automated training and inference pipelines with tools like Kubeflow or MLflow to ensure reproducibility and scalability.
  • Optimize model inference latency and resource usage through techniques like quantization and distillation.
  • Implement monitoring systems to track model drift and performance, establishing feedback loops for continuous improvement.

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