Applied ML Engineer, EDA & Physical AI

Company Description Cruq AI Agent Platform delivers agentic automation that helps enterprises and scale-ups streamline operations, reduce costs, and expand without increasing headcount. The AI-native, modular platform provides a wide library of pre-built agents across functions such as data entry, customer service, compliance, finance, HR, healthcare, IT, legal, and retail. Its AgentOS enables multi-agent collaboration, flexible deployment, and end-to-end workflow automation with high accuracy and continuous operation. The platform integrates with existing enterprise systems to act as a connective layer, offering centralized management, advanced data handling, and customizable solutions tailored to specific industry needs. Cruq AI focuses on delivering measurable efficiency gains and cost savings, making it a strong fit for organizations seeking scalable, reliable intelligent automation.Role Description The Applied ML Engineer, EDA & Physical AI role is a full-time, hybrid position based in Dubai, United Arab Emirates, with flexibility for part-time work from home. The role involves designing, implementing, and optimizing machine learning models focused on exploratory data analysis (EDA) and physical-world AI applications within Cruq AI’s agentic automation platform. Day-to-day responsibilities include developing and evaluating algorithms, building and refining neural network architectures, performing statistical analysis, and collaborating with cross-functional teams to integrate ML components into production systems. The engineer will work closely with product, data, and infrastructure teams to ensure robust deployment, monitoring, and continuous improvement of AI agents. Additional tasks include documentation of models and workflows, contributing to research initiatives, and staying current with advances in applied ML and AI engineering.QualificationsStrong foundation in Computer Science and Algorithms, with experience designing and implementing efficient, scalable solutions.Proficiency in Pattern Recognition and Neural Networks, including applying deep learning methods to real-world data and physical AI use cases.Solid understanding of Statistics for model evaluation, hypothesis testing, and data-driven decision-making.Experience with exploratory data analysis (EDA), data preprocessing, and feature engineering for ML pipelines.Hands-on experience with modern ML frameworks and tools (e.g., PyTorch, TensorFlow, scikit-learn, JAX) and programming in Python or similar languages.Background in deploying ML models into production environments, including familiarity with cloud platforms, MLOps practices, and performance monitoring.Bachelor’s or Master’s degree in Computer Science, Data Science, Electrical Engineering, or a related quantitative field, or equivalent practical experience.Ability to work effectively in a hybrid setting, collaborate with diverse teams, and communicate complex technical concepts clearly to non-technical stakeholders.