• Design and implement a production-ready RAG architecture using Vertex AI, Gemini, and vector databases (Pinecone, Weaviate, ChromaDB) • Enable AI models to safely and reliably use business data from systems like Greenhouse, HubSpot, and QuickBooks • Build ingestion pipelines for unstructured data (documents, emails, logs) • Design agent-based systems and state machines that reason, retain context, and complete multi-step tasks • Build AI-driven workflows for use cases like recruiting operations and financial analysis • Own the architecture of a cloud-native, multi-tenant SaaS/ERP-style platform • Design for data isolation, permissions, auditability, and compliance • Provision and manage infrastructure using Terraform across AWS and GCP • Build backend services using TypeScript/Node.js and Python • Design event-driven systems with queues, retries, idempotency, and DLQs • Integrate external systems via REST/GraphQL APIs, OAuth, and webhooks • Own observability, reliability, and production readiness • Lead code reviews and enforce PR standards • Own GitHub repositories and CI/CD pipelines using GitHub Actions • Implement secure SDLC practices (secrets, environments, branch protection) • Drive release discipline (versioning, tagging, changelogs) • Partner closely with product, leadership, and clients • Translate operational problems into scalable AI architectures with measurable outcomes • Clearly communicate technical decisions to non-technical stakeholders