Senior Machine Learning Engineer

Company Overview

This organization specializes in creating AI-powered solutions focused on defense and infrastructure sectors. Their expertise lies in designing advanced machine learning models targeting supply chain optimization, automation of compliance processes, and predictive analytics, all built to comply with stringent defense-level security protocols. Their innovative platforms combine generative AI techniques with operational workflows to boost efficiency, enable risk management, and improve strategic decision-making in critical industries.

Job Overview

We seek a Senior Machine Learning Engineer to lead the development and deployment of state-of-the-art AI systems for high-stakes initiatives. You will manage the entire machine learning workflow, from initial concept and data handling to model training, fine-tuning, and secure roll-out in production settings. This leadership role involves advancing the synergy between generative AI and classical machine learning approaches across two main projects: an automated requirements engineering platform utilizing large language models (LLMs) for regulatory compliance and rule extraction, and the Intelligent Supply Chain project, which incorporates predictive analytics for demand forecasting, risk evaluation, and procurement optimization. You will operate within an agile framework, ensuring models meet demanding criteria related to accuracy, reliability, interpretability, and defense-grade security standards. Collaboration with diverse teams of data scientists, engineers, and domain specialists is critical to align technical solutions with business goals and maintain sound engineering practices including thorough documentation.

Primary Responsibilities

• Architect and implement LLM pipelines to interpret complex regulatory documents such as military standards and building codes, converting requirements into structured, executable logic.
• Develop Retrieval-Augmented Generation (RAG) systems to facilitate semantic queries across technical documents and historical datasets for enhanced compliance and informed decision-making.
• Innovate prompt engineering methods including few-shot learning and chain-of-thought to boost model effectiveness on specialized tasks while reducing retraining demands.
• Create time-series forecasting models incorporating ERP and external market data to anticipate material demand and spending for optimizing supply chains.
• Design classification and anomaly detection solutions to evaluate supplier risks based on financial, delivery, and geopolitical criteria.
• Formulate multi-objective optimization algorithms balancing cost, lead times, and risk factors within procurement strategies.
• Containerize machine learning models using Docker and Kubernetes and deploy within defensively secured, on-premises inference environments.
• Set up automated training and inference pipelines with tools such as Kubeflow or MLflow to ensure scalability, reproducibility, and compliance with engineering standards.
• Apply optimization techniques like quantization and distillation to reduce inference latency and resource consumption for smooth hardware operation.
• Implement monitoring systems to detect and address model drift or performance degradation, fostering continuous model improvement through feedback loops.

Required Qualifications and Skills

• Minimum of five years’ experience in machine learning engineering with demonstrated production-level model deployment expertise.
• Ability to rapidly assimilate and apply machine learning methodologies within specialized fields including defense, supply chain management, or construction.
• Proven experience working in agile environments using sprint methodologies, adhering to strict documentation and engineering protocols.
• Strong communication skills for effective collaboration with cross-functional teams including data scientists, backend engineers, and domain experts.
• Advanced proficiency in Python and familiar machine learning libraries such as PyTorch, TensorFlow, Scikit-learn, Pandas, and NumPy.
• Expertise in transformer models (BERT, GPT, Llama) and NLP frameworks including Hugging Face and LangChain.
• Hands-on experience with MLOps technologies including containerization (Docker), orchestration (Kubernetes), and experiment tracking (MLflow).
• Competence in designing data preprocessing workflows for both structured (SQL, tabular) and unstructured (text, PDFs) data.
• In-depth understanding of algorithmic design, including graph traversals and geometric computations for custom logic requirements.
• Experience with Retrieval-Augmented Generation architectures and mastery of prompt engineering techniques like few-shot learning and chain-of-thought reasoning.
• Knowledge of time-series forecasting, classification, and anomaly detection methods applied to predictive analytics.
• Familiarity with model optimization strategies such as quantization, distillation, and latency reduction for effective production deployment.
• Practical experience deploying models securely in on-premises inference environments and managing reproducible automated training/inference pipelines.

Work Location

• Onsite presence based in Abu Dhabi, UAE
• Option for remote work available