Start with Python and working with LLM APIs, then move through the practical stack in this order: prompting, retrieval-augmented generation (RAG), building agents with a framework like LangGraph, and finally evaluation and deployment. The fastest way to learn AI engineering is to build small end-to-end projects at every stage — a document chatbot first, then a tool-using agent — since hiring teams increasingly ask for deployed projects, not certificates. A structured course or mentorship helps here, because feedback on real code is what gets you out of tutorial mode.

An AI engineering course teaches you to build production applications on top of large language models rather than train models from scratch. A good one covers Python, prompt engineering, RAG, agentic workflows, evaluation, and deployment, with hands-on projects instead of theory dumps. It is different from a university ML degree, which leans heavily on math and model training — most AI engineering roles today need builders who can ship reliable LLM systems.

It varies widely by company type and experience. Freshers typically start around ₹6–12 LPA at service-based firms, while engineers with 3–6 years of hands-on LLM, RAG, or agent-building experience often earn ₹25–50 LPA at product companies and GCCs, and senior AI engineers can go well beyond that. Specialising in production skills — agentic systems, LangGraph, evaluation, and deployment — pushes you toward the higher end much faster than generic software experience.

AI Engineering by Chip Huyen is the most widely recommended AI engineering book right now — it covers building applications on foundation models, including prompting, RAG, evaluation, and deployment, without drowning you in theory. Pair it with the official LangGraph documentation and, more importantly, with actually building and shipping a small LLM project, since reading alone won't make you interview-ready.

Agentic workflows are AI pipelines where the LLM actively makes decisions across multiple steps — planning a task, calling tools or APIs, checking its own output, and iterating until the goal is met — instead of producing a single one-shot response. They sit between a fixed prompt chain and a fully autonomous agent, which is why they've become the practical pattern for most real systems today, from support automation to coding assistants.

An AI agent is an autonomous system that decides its own actions in a loop, while an agentic workflow is a structured process where LLM-powered steps are orchestrated in a defined flow, often with checkpoints or human review. In practice, teams prefer agentic workflows for production because they are more predictable, testable, and easier to debug, while free-running agents are reserved for open-ended tasks. This distinction is also a common interview topic for AI engineering roles.

Start narrow: pick one repeatable task, break it into steps, and decide where the LLM should reason versus follow fixed logic. Use an orchestration framework like LangGraph to define state, nodes, and conditional edges, connect tools through APIs or MCP, and add guardrails — validation, retries, and human-in-the-loop checkpoints. Then build evaluation (test sets and tracing) before deploying with monitoring, because most agentic workflows fail in production due to weak evaluation, not weak prompts.

Common examples of agentic workflows include customer-support systems that classify tickets, look up orders, and draft resolutions; coding agents that review pull requests and run tests; deep-research assistants that search, read, and compile reports; and document pipelines that extract, validate, and route data across systems. Multi-agent setups — where a planner delegates to specialised agents — are increasingly used for complex research and software engineering tasks.

Use Claude's tool-use capability through the API so the model can call your functions, then orchestrate the multi-step logic with a framework like LangGraph. To connect Claude cleanly to external data sources and internal tools, use MCP (Model Context Protocol), an open standard built for exactly this purpose. Start with a single tool and a simple loop, verify the outputs with evaluations, and only then add more tools and autonomy.

LangGraph is used for building stateful, multi-step LLM applications — agents, multi-agent systems, RAG pipelines with fallbacks, and human-in-the-loop workflows. The LangGraph Python library lets you define a graph of nodes and edges with built-in persistence, so you can add checkpointing, retries, and approval steps — the features that separate a demo from a production system.

First define your state schema — the information that flows through the graph — then create nodes for the LLM call and tool execution, and wire them with edges and conditional edges that decide the next step. Register your tools, compile the graph (with a checkpointer if you need memory), and invoke it with a test query. Once the basic loop works, add evaluation and tracing before expanding to subgraphs or multiple agents.

They solve different problems: LangChain gives you prebuilt components and integrations (models, vector stores, loaders) for LLM apps, while LangGraph is a lower-level orchestration layer for building controllable, stateful agents. For AI engineering roles today, LangGraph is the higher-priority skill because production agent work demands explicit control over flow and state — learn LangChain basics for convenience, but invest your depth in LangGraph.

The LangGraph library itself is open source and free to use — you only pay for the LLM API calls your agent makes, such as OpenAI or Anthropic usage. Optional services around it, like LangSmith for observability or LangGraph Platform for managed deployment, have separate pricing, but you can build and run complete agentic workflows locally without paying anything for LangGraph itself.

Start with the official LangGraph documentation tutorial, which walks you through building your first graph, then follow LangGraph Academy — LangChain's free structured video course — for deeper fundamentals. After that, stop watching and rebuild a small real use case yourself, such as a support agent or research assistant, because LangGraph only truly clicks when you debug your own state and edges.

Expect core concept questions on state management, checkpointers, conditional edges, subgraphs, and human-in-the-loop patterns, plus design questions like when to use a graph over a simple chain, how to persist memory across sessions, and how to debug a failing agent using traces. Interviewers also test production judgment — handling hallucinations, evaluating agent outputs, and controlling latency and cost — so be ready to explain the decisions behind a project you've actually built.