AI/ML Database Engineer

We are seeking a skilled AI/ML Database Engineer residing in Texas with 5 or more years’ experience to design, build, and maintain scalable data systems that support analytics, applications, and emerging AI/ML use cases. This is a hybrid role (working onsite at least 1 day a week) requiring strong expertise in data modeling, data pipelines, and modern database technologies across cloud environments. The ideal candidate combines solid computer science fundamentals with hands-on experience in both traditional and next-generation data platforms. Responsibilities • Design and implement data models and database architectures to support business and technical requirements • Develop, optimize, and maintain ETL processes and data pipelines for reliable data ingestion and transformation • Work with structured and unstructured data across multiple storage technologies • Implement and manage SQL and NoSQL databases for performance, scalability, and reliability • Write clean, efficient Python code for data processing, automation, and integration • Deploy and manage vector databases to support AI/ML and semantic search use cases • Collaborate with data scientists, engineers, and application teams to enable data-driven solutions • Ensure data quality, security, and governance best practices • Monitor and troubleshoot database performance and reliability issues Required • Strong knowledge of data modeling and database design principles • Experience building and maintaining ETL and data pipeline solutions • Solid understanding of data structures (trees, graphs, hash tables, etc.) • Hands-on experience with SQL and NoSQL databases • Proficiency in Python for data engineering tasks • Experience with vector databases (e.g., Pinecone, Weaviate, Chroma) • Familiarity with at least one major cloud platform (GCP, AWS, or Azure) • Strong problem-solving and analytical skills • Good communication and collaboration abilities Preferred • Experience supporting AI/ML or GenAI workloads • Knowledge of data governance and data security frameworks • Experience with big data technologies or distributed systems • DevOps/DataOps experience (CI/CD, containerization, orchestration)