Jobs in Santa Ana, CA

About the role

As a Senior Database Developer – AI/ML Data Systems, you will be a senior member of our database development team, responsible for leading the design, development, optimization, and maintenance of complex database systems that power both traditional business applications and modern AI-driven solutions.

What you'll bring

• You will provide technical expertise and guidance to the team, drive innovation in AI-enabled data engineering, and contribute to the overall data and AI strategy of the organization.
• In this role, you will collaborate with stakeholders, architects, data scientists, and engineering teams to deliver scalable, efficient, and secure database and AI data infrastructure.
• This position requires strong leadership skills, expert-level knowledge of database management systems, hands-on experience with AI/ML data technologies, and the ability to solve complex data challenges.
• 10+ years of progressive experience in database development, administration, and optimization, with a focus on complex database solutions
• Expert-level proficiency in working with relational databases
• demonstrated mastery of T-SQL
• and in-depth knowledge of advanced SQL concepts, database performance tuning, and optimization strategies.
• Hands-on experience with AI/ML data technologies, such as vector databases and embeddings (e.g., Azure AI Search, pgvector, Pinecone), retrieval-augmented generation (RAG) architectures, and LLM APIs (e.g., Azure OpenAI, Anthropic, OpenAI).
• Proficiency in Python for data engineering and AI integration tasks, including working with data and ML libraries (e.g., pandas, LangChain or similar orchestration frameworks) alongside SQL-based development.
• Extensive experience in designing and implementing complex data models, including conceptual, logical, and physical design phases, and a deep understanding of data architecture principles and best practices.
• Strong expertise in designing and implementing large-scale data integration and ETL processes using advanced tools and frameworks (e.g., Azure Data Factory, Informatica, Apache Kafka).
• Familiarity with MLOps concepts and practices, including model data lifecycle management, monitoring data drift, and supporting model training and inference pipelines.

Benefits