• Build data pipelines that scrub PII, create research datasets, and power the research portal for educational AI studies • Architect the path toward self-hosted and on-device model deployments for privacy and global accessibility • Design and implement model orchestration systems that intelligently route requests across multiple AI providers (OpenAI, Anthropic, AWS Bedrock, open-source models) • Build cost optimization infrastructure - implement conversation compression, prompt caching, and smart model selection to keep AI accessible • Create comprehensive observability systems for ML operations - track costs, latency, quality, and usage patterns across thousands of applications • Design and implement infrastructure for fine-tuning and deploying custom models • Build monitoring and alerting systems that help us maintain reliability as AI interactions scale