Phase 1: Remote (Weeks 1–3) — Foundations of AI-First Engineering
- AI-first development methodologies (coding agents, MCP, real-time collaboration tools)
- Retrieval-Augmented Generation (RAG), embeddings, and vector database implementation
- Rapid sprint cycles emphasizing delivery under constraints
Phase 2: Onsite in Austin (Weeks 4–10) — Scaling Production AI Systems
- Agent architectures, evaluation frameworks, verification, and observability (LangChain/LangSmith/LangFuse/CrewAI)
- Enterprise-grade delivery practices: quality assurance, reliability engineering, and high-standards execution
- Fine-tuning and deployment strategies (LoRA/QLoRA + production integration pipelines)
- Multi-agent strategies for modernizing legacy codebases
- Multimodal AI development (image/video/voice) and scalable cloud infrastructure (AWS/Azure)






