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Purpose of Position The Machine Learning Engineer, AI Applications will be responsible for supporting the design, development, testing, and deployment of AI-powered applications that improve business efficiency, customer experience, and decision-making across CASETiFY. The incumbent will work closely with AI engineers, data scientists, product managers, software engineers, and business stakeholders to build practical and scalable AI solutions in a fast-paced and dynamic environment. The incumbent is expected to bring solid technical foundations in machine learning, data processing, and software development, with strong interest in applied AI use cases such as large language model applications, intelligent automation, recommendation, classification, and predictive analytics. This role will contribute to experimentation, solution development, and continuous improvement of AI applications across the organization. Job Description Support the development, testing, and deployment of AI applications for internal productivity, customer experience, and business process optimization. Work closely with AI engineers, data scientists, product managers, and software engineers to translate business problems into practical AI solutions. Assist in building and improving machine learning and AI workflows, including data preparation, feature engineering, model integration, evaluation, and monitoring. Contribute to AI application development using machine learning models, LLM APIs, prompt engineering, retrieval-based approaches, and related technologies where appropriate. Support the implementation of AI use cases such as intelligent search, chatbot or assistant experiences, recommendation, text classification, content generation, anomaly detection, and workflow automation. Help prepare, clean, validate, and transform data for model development, experimentation, and application integration. Participate in model evaluation, experiment tracking, result analysis, and continuous tuning to improve solution quality and business relevance. Collaborate with software engineering teams to integrate AI capabilities into enterprise systems, digital products, and internal tools. Support the setup of development workflows, testing approaches, and basic MLOps practices to improve reproducibility, maintainability, and deployment quality. Assist in monitoring model and application performance, identifying issues, and supporting troubleshooting and improvement activities. Document technical approaches, assumptions, findings, and implementation details to support knowledge sharing and delivery continuity. Stay informed on emerging AI, machine learning, and LLM trends, tools, and best practices relevant to business applications, and contribute positively to team culture through ownership, curiosity, collaboration, and continuous learning. Requirements Solid foundation in machine learning, statistics, data processing, and software engineering principles. Hands-on experience in Python and familiarity with common ML or AI libraries and frameworks used in practical development. Good understanding of applied machine learning workflows including data preparation, model training, evaluation, inference, and performance tracking. Exposure to AI application development such as LLM-based solutions, prompt engineering, retrieval-augmented workflows, recommendation, classification, or predictive analytics is preferred. Familiarity with APIs, backend integration, data pipelines, and application development concepts is a plus. Basic understanding of cloud environments, model deployment, version control, and MLOps concepts is preferred. Good problem-solving and analytical skills with the ability to learn quickly and work on practical business use cases. Able to work collaboratively with cross-functional teams including product, engineering, data, and business stakeholders. Good communication skills with the ability