Alexey Grigorev

How do I transition from Data engineer to AI engineer? Here's a structured 6-step transition path: You already have the hardest part: engineering fundamentals + production mindset. The goal is to add the AI layer on top. Step 1: Work inside an AI-flavored data pipeline - Ingestion + cleaning for RAG / analytics - Chunking, metadata, indexing - Observability and data quality checks Step 2: Learn how model providers work - APIs, limits, retries, rate limits - Latency and cost trade-offs - Privacy and data handling constraints Step 3: Prompting as an engineering discipline - Prompt templates - Versioning + change logs - Structured outputs (JSON schemas) Step 4: Evaluation for generative systems - Golden sets - Automated checks (format, factuality signals, regressions) - Human review loops when it matters Step 5: Tool integration + agentic flows - Function/tool calling - Guardrails (allowed tools, timeouts, fallbacks) - Tracing what the agent did and why Step 6: Build 1-2 small projects end-to-end Examples: - RAG assistant over internal docs - Ticket triage bot with tool calls + evals For an experienced data engineer, this is usually not a multi-year shift. With focused effort + hands-on work, ~3-4 months can be enough to become interview-ready. If you're making this transition, what part feels most unclear: evals, prompting, or agents?