AI & Data Engineer

The AI & Data Engineer role requires strong expertise in designing, building, and maintaining scalable data pipelines and AI systems. Key technical skills include proficiency in programming languages such as Python, Java, or Scala, with a strong emphasis on Python for AI and machine learning tasks. Experience with big data technologies like Apache Spark, Hadoop, and distributed computing frameworks is essential to handle large-scale datasets efficiently. Familiarity with data storage solutions, including SQL and NoSQL databases, data lakes, and cloud storage platforms (AWS, Azure, or Google Cloud), is important for managing diverse data sources. The role demands solid knowledge of machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn to develop, train, and deploy AI models. Strong skills in data preprocessing, feature engineering, and model evaluation are critical. Understanding of MLOps practices, including model versioning, deployment automation, and monitoring, ensures reliable AI system operations. Experience with containerization tools like Docker and orchestration platforms such as Kubernetes supports scalable deployment. The position also requires familiarity with data governance, data privacy standards, and security best practices to maintain compliance. Collaboration skills are important for working with data scientists, software engineers, and business stakeholders to translate requirements into technical solutions. While specific experience and education levels are not detailed, the role typically demands a solid foundation in computer science, data engineering, or related fields, along with practical experience in AI and data engineering projects. Overall, the ideal candidate combines software engineering, data management, and AI expertise to build robust, efficient, and scalable AI-driven data platforms.