Principal AI/ML Engineer

Company Description Cruq AI is an AI-native agent platform that delivers powerful agentic automation to transform operations for enterprises and scale-ups. The platform provides a modular library of pre-built AI agents across domains such as data entry, customer service, compliance, finance, HR, healthcare, IT, legal, and retail, enabling organizations to reduce costs and increase efficiency. Its AgentOS supports multi-agent collaboration, flexible deployment, and end-to-end workflow automation with high accuracy and continuous operation. Cruq AI integrates with existing enterprise systems as a connective layer, offering centralized management, monitoring, and retrieval-augmented data capabilities to maximize the value of current technology investments. The platform is designed for leaders across financial services, supply chain, retail, healthcare, insurance, HR, legal, and IT who seek to accelerate processes, improve accuracy, and maintain a competitive edge through intelligent automation.
Role Description The Principal AI/ML Engineer will lead the design, development, and deployment of advanced machine learning models and agentic architectures that power Cruq AI’s core platform. This full-time remote role involves collaborating with product, engineering, and customer-facing teams to translate business requirements into scalable AI solutions, including multi-agent systems for complex enterprise workflows. Daily responsibilities include designing and optimizing algorithms, building and training neural network models, implementing robust data pipelines, and ensuring performance, reliability, and security of AI services in production. The Principal AI/ML Engineer will guide technical direction, conduct code and architecture reviews, mentor other engineers, and contribute to best practices in experimentation, evaluation, and model monitoring. The role also includes staying current with state-of-the-art AI research, assessing new technologies, and driving innovations that enhance automation capabilities and customer outcomes.
Qualifications
Strong foundation in Computer Science and Algorithms, including data structures, complexity analysis, and distributed systems concepts.
Expertise in Neural Networks and Pattern Recognition, with experience building, training, and deploying deep learning models for real-world applications.
Proficiency in Statistics and applied quantitative methods for model evaluation, experimentation, and performance analysis.
Extensive experience in AI/ML engineering using languages such as Python and frameworks such as PyTorch or TensorFlow.
Hands-on experience designing and operating production-grade ML systems, including data pipelines, feature stores, and model serving infrastructure.
Background in agentic or multi-agent systems, retrieval-augmented generation, or large language models is highly beneficial.
Ability to lead architectural decisions, mentor engineering teams, and communicate complex technical concepts to non-technical stakeholders.
Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Mathematics, or a related technical field; equivalent practical experience is considered.

Work arrangement
Yes