Build Software Using AI as the Primary Instrument
Design, develop, and maintain production-grade SaaS applications using AI agents and tooling as first-class parts of your workflow, not bolt-ons. Use agentic systems to automate development tasks: scaffolding, refactoring, test generation, code review, documentation. Build and maintain MCP servers that connect AI systems to codebases, APIs, databases, and internal tools so AI assistants have reliable, scoped access to the context they need. Design and maintain knowledge repositories (vector databases, retrieval pipelines, embeddings) that give AI systems accurate, current, domain-specific grounding. Apply these tools across the full SDLC: from architecture drafting and requirements analysis through implementation, testing, and deployment.
Architect Systems, Not Just Features
Own architecture decisions end-to-end. Design service boundaries, API contracts, data models, and integration patterns with long-term maintainability in mind. Reason through tradeoffs explicitly and document them so others can understand the reasoning, not just the outcome. Design for observability, reliability, and failure from the start. Identify and address structural technical debt before it compounds.
Build and Maintain SaaS Products
Design, develop, and maintain scalable line-of-business SaaS applications. Build backend services, APIs, and data models. Ensure system performance, reliability, and security. Translate operational business needs into well-architected software solutions.