Shaped SWE 2026

AI Engineering Roadmap

3 min read

🤖 AI Engineering Roadmap (2026)

Positioning

Most companies don’t need you to train models. They need engineers who can build reliable, secure, cost-efficient products on top of LLMs. That is software engineering + distributed systems + evaluation discipline. Your backend depth is your edge.

Layers (core → advanced)

  1. Foundations: LLM Fundamentals: tokens, embeddings, transformers intuition, sampling, context windows, limitations
  2. Integration: API calls, streaming, structured output, tool calling, retries/timeouts, cost tracking (Spring AI, Go/Python SDKs)
  3. Context engineering: prompts, system prompts, few-shot examples, managing long context, memory
  4. Retrieval: RAG: chunking, embeddings, vector + hybrid search, reranking, citations, GraphRAG (awareness)
  5. Workflows & agents: Agents & Workflows · LangChain & LangGraph: chains → routers → orchestrator-workers → autonomous agents; human-in-the-loop; durable execution
  6. Interoperability: MCP (tools/resources/prompts), A2A (agent-to-agent protocol)
  7. Production: Evals, Guardrails & LLMOps: evals, tracing, guardrails, prompt-injection defense, LLM gateways, semantic caching, cost/latency optimization
  8. Emerging: multimodal (vision, voice/realtime APIs), computer use / browser agents, small/local models (Ollama, vLLM), fine-tuning (LoRA) awareness, reasoning models and “thinking” budgets, AI coding agents in the SDLC

AI-native engineering habits (use AI to become a better engineer)

  • Use AI coding assistants (Claude Code, Cursor, Copilot) daily, but review every line. Interviewers still test whether you understand
  • Use AI to generate test cases, explain unfamiliar code, and critique your designs, then verify with docs
  • Never outsource the learning loop: solve the DSA problem first, then ask the AI for a critique

LLM system design questions to practise

  • Design an enterprise RAG search over 10M documents with access control
  • Design a customer-support agent that can take actions (refunds) safely
  • Design an LLM gateway for 50 internal teams (routing, quotas, caching, observability, PII redaction)
  • Design an evaluation platform for prompts/agents

🧪 Labs (🟢 warm-up → 🟡 core → 🔴 hard → ⚫ boss)

  • 🟡 All 12 Orbit use cases (UC1–UC12) with an eval suite each
  • 🔴 The same task as a chain vs orchestrator–workers vs an autonomous agent: a quality/cost/latency table
  • ⚫ Red-Team Day + a 40% cost cut with quality held

🧠 Cognitive tasks

  • Every AI feature starts with: “What’s the simplest pattern that works, and how will I measure it?”

🛰️ Orbit integration

Go deeper

⚙️ LLM Inference Internals · 🧩 AI & Agent Patterns · 🛰️ Orbit - Use Cases

Resources

  • ⭐ AI Engineering (Chip Huyen) · Hands-On Large Language Models (Alammar & Grootendorst)
  • ⭐ Anthropic: “Building effective agents”, prompt engineering guide, tool use docs, “Effective context engineering for AI agents”
  • LangChain Academy (free) · DeepLearning.AI short courses (free) · modelcontextprotocol.io
  • Karpathy: Neural Networks: Zero to Hero, Let’s build GPT, Intro to LLMs ⭐
  • 3Blue1Brown: neural networks / transformers series
  • Blogs: Eugene Yan, Hamel Husain, Simon Willison, Chip Huyen, Lilian Weng

Graph View

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