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Start with Pinecone if you want the fastest path to production with minimal ops overhead. It's fully managed and has the best developer experience. Learn Chroma for local development and prototyping. Consider Weaviate if you need hybrid search or want to understand more architectural concepts. The fundamentals (embeddings, indexing strategies, similarity metrics) transfer across all platforms.

Yes, but not deeply. You need to understand what embeddings are (dense vector representations of data), how they capture semantic meaning, and why dimension size matters. You don't need to build embedding models from scratch. Focus on using embedding APIs (OpenAI, Cohere, Sentence Transformers) and understanding when different models work better for different use cases.

With focused effort: 2-4 weeks to understand core concepts and build basic RAG applications. 6-8 weeks to handle production concerns like chunking strategies, hybrid search, metadata filtering, and cost optimization. Most developers waste time bouncing between tutorials. A structured learning path with real projects gets you job-ready faster.

In 2026, most AI engineering roles mention: experience with at least one vector database, understanding of embedding models and chunking strategies, ability to build and optimize RAG pipelines, knowledge of similarity search algorithms (ANN, HNSW). Senior roles also want production experience with scaling, cost management, and evaluation metrics for retrieval quality.

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.

Vector databases are the memory layer for RAG (Retrieval Augmented Generation). You embed your documents, store them in a vector database, then query for semantically similar chunks when a user asks a question. These chunks become context for the LLM. Understanding this pipeline end-to-end is the most valuable skill for AI engineering roles building production applications.

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.