Teams at SqlDBM

Lead engineering strategy and architecture for SqlDBM v2, including security, scalability, AI enablement, DevOps, CI/CD, testing, observability, and deployment workflows. Hire, coach, and unblock full-stack engineering teams while maintaining React code quality and hands-on .NET development. Establish AI tooling standards, guardrails, productivity metrics, and reliable production delivery practices.

Design and build the agentic layer of an enterprise data modeling platform, including agent loops, tool interfaces, context selection, MCP integrations, permissions, attribution, auditing, and evaluation infrastructure. Develop production AI systems that operate safely on governed enterprise data models with human oversight. Work across strongly typed languages, AWS, APIs, and production systems in a distributed senior engineering team.

Build backend systems powering enterprise AI capabilities for data modeling, schema generation, documentation, governance, and metadata workflows. Develop .NET microservices, REST APIs, MCP servers, LLM integrations, context management, output validation, request orchestration, usage metering, and compliance-aware workflows on AWS. Collaborate directly with product and architecture leadership in a remote-first team.