Senior Database Developer – AI/ML Data Systems

Profit Recovery Partners•Santa Ana, CA

9d•Onsite

About The Position

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. 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.

Requirements

10+ years of progressive experience in database development, administration, and optimization, with a focus on complex database solutions; recent hands-on experience building or supporting AI/ML-enabled data solutions strongly preferred.
Bachelor's degree in Computer Science, Information Systems, Data Science, or a related field preferred.
Advanced certifications in database management systems or AI/data engineering (e.g., Microsoft Certified: Azure Data Engineer Associate, Azure AI Engineer Associate, Oracle Certified Master) preferred.
Expert-level proficiency in working with relational databases, preferably Microsoft SQL Server; 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.
Understanding of responsible AI principles, including data privacy, bias mitigation, and governance considerations for AI systems handling sensitive business data.
Demonstrated leadership skills with the ability to lead and mentor a team of database developers, providing technical guidance, conducting code reviews, and fostering professional growth.
Expert-level proficiency in advanced database administration tasks, such as performance monitoring, capacity planning, backup and recovery strategies, and high availability configurations.
Exceptional problem-solving and analytical skills to identify and resolve complex database-related issues and data challenges.

Nice To Haves

Bachelor's degree in Computer Science, Information Systems, Data Science, or a related field
Advanced certifications in database management systems or AI/data engineering (e.g., Microsoft Certified: Azure Data Engineer Associate, Azure AI Engineer Associate, Oracle Certified Master)

Responsibilities

Lead the design, development, and implementation of complex and highly scalable database systems, including data models, schemas, tables, views, and stored procedures, supporting both transactional workloads and AI/ML applications.
Architect and implement data infrastructure for AI solutions, including vector databases, embedding stores, and semantic search capabilities (e.g., Azure AI Search, pgvector, Pinecone) to support retrieval-augmented generation (RAG) and other LLM-powered applications.
Design and build data pipelines that prepare, transform, and serve high-quality data for machine learning models, LLM fine-tuning, and analytics, including feature engineering and feature store management.
Integrate AI services and APIs (e.g., Azure OpenAI, Anthropic, OpenAI) into database-driven applications and workflows, including intelligent document processing, natural language querying, and automated data classification.
Leverage AI-assisted development tools (e.g., GitHub Copilot, AI code assistants) to accelerate development, improve code quality, and champion responsible adoption of these tools across the team.
Collaborate with cross-functional teams, including software developers, data scientists, system administrators, and business analysts, to understand requirements, provide technical expertise, and deliver data and AI solutions that meet business objectives.
Provide technical support and training to users and clients on the use of data systems, AI-enabled tools, processes, and custom applications built.
Conduct advanced performance tuning and optimization activities, including query optimization, indexing strategies (including vector indexing), and database configuration enhancements, to maximize system performance and efficiency.
Develop and implement proactive monitoring solutions to detect and resolve performance issues, system bottlenecks, and database errors in a timely manner.
Investigate and resolve complex database-related issues, including data anomalies, performance bottlenecks, and system failures, using advanced troubleshooting techniques and tools.
Contribute to data and AI architecture standards, guidelines, and governance frameworks, ensuring data consistency, integrity, interoperability, and responsible AI practices across the organization.
Develop and implement robust security measures, access controls, and data encryption techniques to protect sensitive data including data used in AI systems and ensure compliance with industry regulations, data privacy requirements, and best practices.
Provide technical leadership, mentorship, and guidance to junior members of the database development team, fostering a collaborative and knowledge-sharing environment.
Stay abreast of emerging database and AI technologies, trends, and industry best practices; evaluate their applicability to the organization's data landscape and propose innovative solutions.
Develop and implement data quality assurance processes, data cleansing techniques, and data governance strategies to maintain high-quality data for both operational systems and AI/ML training and inference.
Design and implement robust data integration and ETL processes, leveraging advanced tools and technologies (e.g., Azure Data Factory, Informatica, Apache Kafka) to ensure seamless data flow and integration across multiple systems and sources.
Provide expert-level database administration, including capacity planning, backup and recovery strategies, disaster recovery planning, and high availability configurations.
Perform other duties as assigned.