Phase 1: Remote (Weeks 1–3) — Foundations of AI-First Engineering
- AI-native development workflows (coding agents, MCP, real-time collaboration tools)
- Retrieval-Augmented Generation (RAG), embeddings, and vector database implementation
- Fast-paced project sprints emphasizing delivery under resource and time constraints
Phase 2: Onsite in Austin (Weeks 4–10) — Scaling Production AI Systems
- Agent architectures, evaluation frameworks, verification, and observability tools (LangChain/LangSmith/LangFuse/CrewAI)
- Enterprise-standard delivery: quality assurance, system reliability, and high-bar execution practices
- Fine-tuning and deployment strategies (LoRA/QLoRA + production-level integration)
- Multi-agent modernization applied to existing production codebases
- Multimodal AI development (image/video/voice processing) and scalable cloud infrastructure (AWS/Azure)






