Role roadmaps / AI Engineer Role flowchart
Ship products on pretrained models: retrieval, eval, and guardrails. The hard parts are data, not the model brand.
Also called: Applied AI Engineer, LLM Application Engineer
You ship: A retrieval pipeline with evals, access control, and a rollback — not a chat demo.
Faster to demo, slower to production than people admit
Roadmap Coding Interview Question bank Switch roleData Analyst BI Analyst Data Engineer Analytics Engineer Data Scientist ML Engineer MLOps Engineer AI Engineer Data Architect Nearby roles:ML Engineer , Data Engineer , Data Scientist
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Start · AI Engineer Foundations RAG is ETL Chunk, embed, version, ACL-filter. Treat the index like a table. Warehouse-native LLMs COMPLETE in incremental jobs, never in a dashboard refresh. Eval before vibes Golden questions, faithfulness, and a human sample. Latency is a metric. Daily craft Prompts as code Version them. Test them. Do not hide them in Slack. Retrieval quality Hybrid search, metadata filters, and “why this chunk?” Safety and ACL The model must not see rows the user cannot. Filters happen before generation. How you ship Traces, not screenshots Log retrieval, tokens, and thumbs-down. Cost per answer. Orchestrate like a pipeline Airflow or a job for re-embed. The chatbot is the last mile. Career moves AI engineer interview A RAG design, eval, and “what happens when the source table changes?” Next role MLE if you train. DE if retrieval was the interesting part. Product if you like users. Keep practicing Study this node
RAG is ETL Chunk, embed, version, ACL-filter. Treat the index like a table.
Chunk, embed, version, ACL-filter. Treat the index like a table.
RAG is ETL: chunk, embed, version, ACL-filter. Treat the index like a table.
Time to learn Chunk, embed, version, ACL-filter. Index is a table.
Prerequisites Grain of a chunk Who may retrieve it What you need RAG pipelines article AI questions A corpus with an owner Concepts to study Chunking as a modeling choice Embedding model versioning Index refresh as a pipeline ACL filters before retrieval Citations / chunk IDs as grain Tech stacks Warehouse or object store as source — System of recordVector DB or warehouse vectors — IndexOrchestrator — RefreshHow to study RAG pipelines article; AI interview questions. Draw source → chunk table → embed job → index → query with ACL. What to produce A chunk grain sentence An ACL filter before embedding or at query Practice on this site RAG pipelines; AI questions Pitfalls Re-embedding in the request path No ACL so the model sees rows the user cannot Unversioned chunks Interview prompts What is the grain of a chunk, and who is allowed to retrieve it? Links
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