Phase 1: Remote Phase (Weeks 1–3) — Foundations in AI-First Engineering
- AI-native development workflows (coding agents, MCP, real-time collaboration tools)
- Retrieval-Augmented Generation (RAG), embeddings, and vector database architectures
- High-velocity project sprints emphasizing delivery under constraints
Phase 2: On-Site in Austin (Weeks 4–10) — Scaling Production AI
- Agent architectures, evaluation systems, verification frameworks, and observability tooling (LangChain/LangSmith/LangFuse/CrewAI)
- Enterprise-level execution: quality assurance, system reliability, and high-standard delivery
- Fine-tuning and deployment strategies (LoRA/QLoRA + production-ready integration)
- Multi-agent approaches to modernizing legacy codebases
- Multimodal AI implementations (image/video/voice) and scalable cloud infrastructure (AWS/Azure)






