Senior Machine Learning Engineer

Role Overview

Automated Requirements Engineering

• Build LLM and NLP pipelines to parse regulatory texts such as military standards and building codes.
• Extract structured rules and convert natural-language requirements into executable logic tuples.
• Use RAG for semantic search across technical documentation and historical project data.
• Apply few-shot learning and chain-of-thought prompt strategies to domain-specific tasks.

Intelligent Supply Chain

• Forecast material demand and spend using ERP data and external market signals.
• Assess supplier risks using financial health, delivery performance, and geopolitical factors.
• Optimize procurement decisions across cost, lead time, and risk.

MLOps and Production Deployment

• Deploy containerized models using Docker and Kubernetes in secure on-premise environments.
• Orchestrate reproducible, scalable training and inference pipelines with tools such as Kubeflow or MLflow.
• Optimize inference latency and resource efficiency using quantization and distillation.
• Monitor model drift and performance and establish continuous-improvement feedback loops.

Professional Qualifications

• Five or more years of Machine Learning Engineering experience with a record of production model deployment.
• Ability to apply ML techniques to domains such as defense engineering, supply chain, or construction.
• Experience with agile sprints, rigorous engineering standards, and documentation.
• Strong communication and collaboration with data scientists, backend engineers, and domain experts.

Required Technical Skills

• Expert Python and standard ML libraries: PyTorch, TensorFlow, Scikit-learn, Pandas, and NumPy.
• Transformer architectures including BERT, GPT, and Llama; NLP frameworks including Hugging Face and LangChain.
• MLOps proficiency, including Docker, Kubernetes, and MLflow.
• Data preprocessing for structured SQL and tabular data and unstructured text and PDFs.
• Algorithmic knowledge including graph traversal and geometric computations.
• RAG architectures, prompt engineering, forecasting, classification, anomaly detection, and model optimization.
• Secure on-premise model deployment and automated training and inference pipelines.

Work Location

• Onsite location: Abu Dhabi, UAE.

Job Details

• Work type: Full-time.
• Role level is listed as Mid-Level in job details; the title and scope identify the role as senior.