Job Description: We are seeking a proactive and skilled Cloud/AI Engineer to join our dynamic team. This is a critical role, requiring individuals who are eager to learn quickly and possess the technical acumen to understand, manage, and optimize Python-based code and data workflows. Your primary responsibility will be ensuring seamless operations for our data systems, while building scalable and innovative solutions that align with business goals. Key Responsibilities: • Learn and adapt quickly to new tools, technologies, and domain-specific knowledge to excel in the role. • Manage and maintain Python-based code repositories, understanding their functionality and ensuring smooth operation without disruption. • Oversee and optimize workflows associated with Microsoft Fabric, Azure Synapse Analytics, PySpark, and other cutting-edge tools. • Monitor and ensure the reliability and performance of data systems—ensuring seamless execution of ETL/ELT pipelines and batch processes. • Develop a strong understanding of data models to support efficient data storage, transformation, and retrieval processes. • Implement, manage, and optimize machine learning models, working closely with data scientists on tasks such as training, deployment, evaluation, and monitoring. • Collaborate with stakeholders to define data processing requirements and ensure alignment with business objectives. • Apply principles from machine learning practices to solve practical business challenges using structured and unstructured data. • Stay updated on emerging trends in the data engineering and machine learning domains. Required Skills: • Technical Expertise: • Proficiency in Python programming with the ability to debug, maintain, and enhance existing codebases. • Experience with PySpark for distributed data processing and data engineering tasks. • Familiarity with Microsoft Fabric and Azure Synapse Analytics, particularly for handling analytics workloads and cloud-based data architectures. • Strong understanding of relational and non-relational data models. • Solid grasp of ML lifecycle concepts and experience in implementing machine learning models in a production environment.