What should I learn to become a solo AI SaaS builder?</strong></p><p></p>

I'm trying to become a strong end-to-end AI product engineer rather than just someone who can build LLM demos.

My goal is to be able to take an AI SaaS idea from zero → MVP → production → paying users, largely as a solo developer, using agentic/multi-agent AI where it actually makes sense.

I'm currently comfortable with software development and have been working with LLMs, RAG, Semantic Kernel/agent workflows and Python, but I want to systematically fill the gaps required to build and operate a real product.

I'm particularly interested in learning:

• LLM engineering and context engineering

• Tool/function calling

• RAG and production retrieval

• Single-agent vs multi-agent architectures

• Agent orchestration, planning, memory and HITL

• AI evaluation and observability

• MCP and tool ecosystems

• Real-time AI / streaming / WebSockets / SSE

• FastAPI + PostgreSQL + Redis + queues

• Authentication, multi-tenancy and SaaS architecture

• Payments, subscriptions and usage-based billing

• Docker, CI/CD and cloud deployment

• AI security and prompt/tool injection

• AI cost optimization and model routing

• Product discovery, analytics, pricing and iteration

• Eventually voice AI, STT/TTS and more advanced agent systems

For people who are actually building AI SaaS products: what skills would you consider essential, what would you skip initially, and what resources/projects helped you become productive?

I'm especially looking for practical resources and project-based learning paths, rather than a collection of courses.

If you were starting from my position today, what would your 3–6 month learning/building roadmap look like?