We are seeking an experienced Database Administrator (DBA) to join our team, responsible for evaluating, selecting, and managing databases for a high-performance machine learning project. The ideal candidate will have extensive knowledge in managing and optimizing databases, particularly for use with machine learning models like Random Walk, PC Analysis, LSTM, GRU, and LLM/ANI integration. You will play a critical role in determining the appropriate database systems and ensuring their seamless integration with our API-driven machine-learning architecture.
𝐊𝐞𝐲 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬:
Evaluate and decide on the most suitable database technology (e.g., SQL, NoSQL, Time-series Databases) based on project needs.
Design and implement efficient database structures to support machine learning models like Random Walk, PC Analysis, LSTM, GRU, and AI-based tools.
Optimize database performance for high-volume, time-series, and real-time data operations, particularly for machine learning and predictive models.
Manage the integration of databases with APIs and the machine learning pipelines, ensuring smooth data flow between systems.
Ensure scalability and reliability of databases for large datasets and high-performance processing.
Develop and implement backup, disaster recovery, and data security protocols for critical data systems.
Work closely with the development team to ensure database schemas and queries are optimized for data retrieval and processing.
Monitor and troubleshoot database systems to ensure high availability and performance.
Stay up-to-date with the latest trends in database technologies, especially in the context of machine learning and artificial intelligence.
𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭𝐬:
Proven experience in database management and design, with expertise in both SQL and NoSQL systems (e.g., MySQL, PostgreSQL, MongoDB, Cassandra, etc.).
Strong knowledge of time-series databases (e.g., InfluxDB, TimescaleDB) for handling sequential data in machine learning projects.
Experience in database management for projects involving machine learning models like LSTM, GRU, and AI-based systems.
Familiarity with cloud-based database solutions (e.g., AWS RDS, Google Cloud SQL, Azure SQL) for scalability and data management.
Ability to decide on appropriate database systems based on project needs, taking into account performance, cost, and scalability.
Proficiency in writing complex queries, stored procedures, and managing database triggers and indexes.
Understanding of data models and schemas, particularly in the context of machine learning and AI.
Strong data security and backup management skills.
Experience in API integration with databases, ensuring secure and efficient data retrieval and updates.
𝐏𝐫𝐞𝐟𝐞𝐫𝐫𝐞𝐝 𝐐𝐮𝐚𝐥𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧:
Experience in time-series data management for projects involving predictive modeling and AI integration.
Knowledge of database management in environments utilizing LLM (Large Language Models) and ANI (Artificial Narrow Intelligence).
Proficiency in cloud-based database solutions and database performance tuning.
Familiarity with tools like Docker, Kubernetes, or other containerization technologies for database deployment