Phase 1: Remote Phase (Weeks 1–3) — Foundations in AI-Native Engineering
- Workflows centered on AI-native development (coding agents, Model Context Protocol, real-time collaboration tools)
- Retrieval-Augmented Generation (RAG) systems, embeddings, and vector database integration
- Fast-paced project sprints emphasizing delivery under tight constraints
Phase 2: Onsite Phase in Austin (Weeks 4–10) — Scaling Production AI
- Agent architectures, evaluation frameworks, verification techniques, and observability tooling (LangChain/LangSmith/LangFuse/CrewAI)
- Enterprise-level execution standards: quality assurance, reliability engineering, and rigorous delivery practices
- Fine-tuning methodologies and deployment strategies (LoRA/QLoRA with production integration)
- Multi-agent strategies for modernizing legacy codebases
- Multimodal AI implementations (image/video/voice processing) and scalable cloud infrastructure (AWS/Azure)






