Qualifications:? Bachelor's degree in a relevant field like Computer Science or Data Analytics with at least 1 year of hands-on experience, or 4-years of applicable project work.? Design and develop the overall data management architecture and pipeline for vector database applications.? Build processes for ingesting large volumes of vectorized data from various structured and unstructured sources.? Implement data cleaning, preprocessing, and vectorization pipelines using Python, Spark, etc.? Store and index large-scale vector datasets using vector databases like Milvus, Pinecone, Weaviate etc.? Optimize vector indexing strategies for efficient similarity search and retrieval.? Engineer data processing workflows to analyze large vector spaces using dimensional reduction techniques.? Create APIs for vector data access and integration with downstream applications and models? Implement infrastructures on scalable cloud platforms like AWS, GCP for distributed vector data storage and processing.? Continuously monitor and optimize the performance of vector database solutions? Proficiency in programming languages relevant to data engineering, particularly Python, Java, SQL, and JSON.? Hands-on experience with various database technologies, including but not limited to MySQL, MongoDB, and SQL Server, for both relational and noSQL data storage.? Demonstrated expertise in building and maintaining data pipelines, specifically for data ingestion and transformation.? Solid grasp of the software development life cycle, emphasizing testing, documentation, and operational support.? Working experience with cloud technologies, preferably Azure or AWS, to manage and scale data platforms.? Skill in query optimization and data modeling techniques to ensure efficient data storage and retrieval.? Familiarity with machine learning frameworks like TensorFlow or Scikit-Learn to support data science initiatives.