Learn LlamaIndex for RAG Jobs
The Framework Employers Want.

LlamaIndex was built from the ground up for RAG and document-based AI applications. It offers superior abstractions for indexing, retrieval, and response synthesis compared to general-purpose frameworks. In 2026, companies building serious RAG systems often prefer LlamaIndex for its production-focused features like evaluation modules, structured outputs, and enterprise integrations. Demonstrating LlamaIndex expertise signals you understand RAG deeply, not just at a tutorial level.

RAG skills are in demand across multiple roles: AI Engineer, Machine Learning Engineer, LLM Engineer, Applied AI Scientist, AI Platform Engineer, and increasingly Senior Software Engineer positions at AI-forward companies. Job postings may not always mention LlamaIndex by name, but they'll list requirements like 'experience with RAG systems,' 'document retrieval pipelines,' or 'LLM application development.' LlamaIndex expertise directly maps to these requirements.

For developers with Python experience and basic ML knowledge: 4-8 weeks of focused learning to reach job-ready status. The first 2 weeks cover RAG fundamentals and core LlamaIndex concepts. Weeks 3-4 dive into advanced patterns like agents, routers, and evaluation. Weeks 5-8 focus on building portfolio projects that demonstrate production thinking. With 1:1 coaching, this timeline can compress significantly by avoiding common pitfalls and focusing on what hiring managers actually evaluate.

Skip the basic 'chat with PDF' projects everyone builds. Instead: 1) A multi-document RAG system with hybrid search and reranking that handles messy real-world data, 2) A RAG evaluation pipeline that measures retrieval quality and answer accuracy, 3) An agentic RAG system that routes queries and uses tools. Document your architectural decisions, trade-offs, and how you'd scale each system. This demonstrates production thinking that gets you past technical screens.

Both are viable, but LlamaIndex has stronger RAG-specific abstractions. LangChain is more general-purpose (agents, chains, memory), while LlamaIndex excels at document ingestion, indexing strategies, and retrieval optimization. For RAG-focused roles, LlamaIndex demonstrates specialized depth. That said, the underlying concepts transfer. Master one framework deeply rather than knowing both superficially. Hiring managers prefer depth over breadth.

Not necessarily. While some programming experience is helpful, many of my clients have successfully transitioned from web development, data science, or other technical backgrounds. We'll assess your current skills during our strategy call and create a personalized plan that meets you where you are.

Most clients invest 10-15 hours per week, but this can be flexible based on your schedule. We'll have weekly 1:1 calls plus time for you to work on projects and learning. The key is consistency. Regular, focused effort beats occasional marathons.

You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.

Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.

Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.

Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.