AI Engineer US shift

Role Overview: As a full-time remote AI Engineer at TriNexa on the US shift, your primary responsibility will be to design, build, and deploy AI models and pipelines for real-world business use cases across various industries. You will work on researching and prototyping machine learning and deep learning solutions, optimizing neural networks and NLP systems, and integrating these models into production software environments. Collaboration with cross-functional teams such as product, data engineering, and software development will be essential to define requirements, evaluate performance, and enhance reliability and scalability of AI systems. Additionally, you will contribute to code reviews, documentation, model monitoring, and continuous improvement of the AI engineering toolkit and best practices. Key Responsibilities: - Research and prototype machine learning and deep learning solutions for real-world business use cases. - Design and optimize neural networks and NLP systems for classification, prediction, or recommendation tasks. - Collaborate with cross-functional teams to define requirements, evaluate performance, and enhance reliability and scalability of AI systems. - Contribute to code reviews, documentation, model monitoring, and continuous improvement of the AI engineering toolkit and best practices. Qualifications: - Strong foundation in Computer Science and Software Development, including algorithms, data structures, version control, and production-grade coding practices. - Hands-on experience with Neural Networks and Pattern Recognition, including training, tuning, and deploying deep learning models. - Practical expertise in Natural Language Processing (NLP), such as text classification, information extraction, and working with modern NLP frameworks. - Proficiency with common AI/ML tools and frameworks such as Python, PyTorch or TensorFlow, scikit-learn, Docker, and cloud platforms like AWS, GCP, or Azure. - Bachelors or higher degree in Computer Science, Data Science, Engineering, or a related technical field, or equivalent practical experience. - Experience building and deploying models into production environments, including working with APIs, microservices, and CI/CD pipelines. - Strong analytical and problem-solving skills with the ability to translate business requirements into robust AI solutions. - Effective written and verbal communication skills and the ability to collaborate with distributed cross-functional teams. - Familiarity with MLOps practices, monitoring and observability for models, and responsible AI considerations such as fairness, bias, and explainability is a plus. Role Overview: As a full-time remote AI Engineer at TriNexa on the US shift, your primary responsibility will be to design, build, and deploy AI models and pipelines for real-world business use cases across various industries. You will work on researching and prototyping machine learning and deep learning solutions, optimizing neural networks and NLP systems, and integrating these models into production software environments. Collaboration with cross-functional teams such as product, data engineering, and software development will be essential to define requirements, evaluate performance, and enhance reliability and scalability of AI systems. Additionally, you will contribute to code reviews, documentation, model monitoring, and continuous improvement of the AI engineering toolkit and best practices. Key Responsibilities: - Research and prototype machine learning and deep learning solutions for real-world business use cases. - Design and optimize neural networks and NLP systems for classification, prediction, or recommendation tasks. - Collaborate with cross-functional teams to define requirements, evaluate performance, and enhance reliability and scalability of AI systems. - Contribute to code reviews, documentation, model monitoring, and continuous improvement of the AI engineering toolkit and best practices. Qualifications: - Strong foundation in Computer Science and Software Development, including algorithms, data structures, version control, and production-grade coding practices. - Hands-on experience with Neural Networks and Pattern Recognition, including training, tuning, and deploying deep learning models. - Practical expertise in Natural Language Processing (NLP), such as text classification, information extraction, and working with modern NLP frameworks. - Proficiency with common AI/ML tools and frameworks such as Python, PyTorch or TensorFlow, scikit-learn, Docker, and cloud platforms like AWS, GCP, or Azure. - Bachelors or higher degree in Computer Science, Data Science, Engineering, or a related technical field, or equivalent practical experience. - Experience building and deploying models into production environments, including working with APIs, microservices, and CI/CD pipelines. - Strong analytical and problem-solving skills with the ability to translate business requirements into robust