Senior AI Data Engineer

Required Skills And Qualifications Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or related field. 5-7 years of hands-on experience in data engineering, with at least 2 years focused on AI/ML workloads. Expert proficiency in Python and experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn. Strong experience with Databricks, Apache Spark, and distributed computing for ML workflows. Deep understanding of the machine learning lifecycle, including model training, deployment, and monitoring processes. Experience with feature engineering, data preprocessing techniques, and ML data pipelines. Knowledge of vector databases, embeddings, and similarity search for AI applications. Proficiency in SQL for structured and unstructured data management. Understanding of data governance, model governance, and AI ethics principles. Strong analytical and problem-solving capabilities with attention to data quality. Excellent collaboration skills for working with data scientists, ML engineers, and architects. Required Skills And Qualifications Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or related field. 5-7 years of hands-on experience in data engineering, with at least 2 years focused on AI/ML workloads. Expert proficiency in Python and experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn. Strong experience with Databricks, Apache Spark, and distributed computing for ML workflows. Deep understanding of the machine learning lifecycle, including model training, deployment, and monitoring processes. Experience with feature engineering, data preprocessing techniques, and ML data pipelines. Knowledge of vector databases, embeddings, and similarity search for AI applications. Proficiency in SQL for structured and unstructured data management. Understanding of data governance, model governance, and AI ethics principles. Strong analytical and problem-solving capabilities with attention to data quality. Excellent collaboration skills for working with data scientists, ML engineers, and architects. Preferred/Nice-to-Have Skills Experience with generative AI applications, including RAG (Retrieval-Augmented Generation) and fine-tuning. Knowledge of LangChain, HuggingFace, or other GenAI frameworks. Familiarity with Azure ML, AWS SageMaker, or Google Vertex AI platforms. Experience with graph databases (Neo4j, Amazon Neptune) for knowledge graph implementation. Understanding of AI model explainability and interpretability techniques. Experience with A/B testing frameworks for ML model evaluation. Certification in Databricks, AWS, Azure, or Google Cloud Platform AI/ML services. Publications or contributions to open-source ML projects #J-18808-Ljbffr