Paolo Perrone recently shared something that caught my attention: "AI engineers make $180K median. Senior at OpenAI? $860K-$1.27M. But most people try to break in by learning the wrong skills in the wrong order. Here's the actual roadmap."

That "wrong order" point is the real value. The internet pushes you to jump straight into fancy demos, agent hype, or model training tutorials. Paolo's post cuts through it: most companies are not hiring you to invent new neural networks. They are hiring you to make models work reliably in production, with costs, latency, safety, monitoring, and maintainability handled like any other software system.

"You're not inventing neural networks. You're not doing PhD research. Companies need people who make models work in production."

Below is my expanded take on Paolo Perrone's four-phase roadmap, with extra context, examples, and a practical way to execute it without getting lost.

The core idea: sequence beats intensity

If you want to become an AI engineer in 2026, the biggest advantage is not raw intelligence or grinding 12 hours a day. It is learning the right skills in the right sequence, so each phase compounds.

Paolo's roadmap works because each phase answers a different hiring question:

Can you write and ship software? (Fundamentals)
Can you use LLMs effectively and predictably? (LLM integration)
Can you run AI features in production? (Production systems)
Can you prove it with projects and clear communication? (Get hired)

Phase 1 (1.5-3 months): Fundamentals that remove future pain

Paolo Perrone starts with Python, APIs, JSON, errors, Git, and basic ML concepts. That may sound boring, but it is exactly what prevents months of frustration later.

What "production-ready Python" really means

Tutorial Python often skips the parts that break in real apps. Production-ready Python means you can:

Structure a project (modules, packages, dependency management)
Write clean functions with clear inputs and outputs
Log what matters and surface errors cleanly
Write a few tests for critical logic

A simple benchmark: can you build a small CLI or web service that calls an API, validates inputs, retries on failure, and returns a useful error message? If not, do not rush forward.

APIs, JSON, and error handling are LLM engineering basics

Most LLM work is orchestration. Your day-to-day will include request payloads, response schemas, timeouts, rate limits, and partial failures. Practice by integrating two or three non-AI APIs (payments, CRM, email, analytics). If you can do that, LLM APIs become just another dependency.

Basic ML concepts, but only what you need

Paolo mentions "what a model is, training vs inference, embeddings, core terminology." In 2026, you do not need to be a researcher to be employable, but you do need vocabulary and intuition:

Training vs inference: why inference cost and latency dominate product decisions
Embeddings: why semantic search and RAG are possible
Evaluation basics: accuracy is not enough, you need task metrics and failure analysis

"Master this or waste months later."

Phase 2 (2-3 months): LLM integration, not prompt "tricks"

Paolo Perrone calls out a common misconception: "Prompt engineering" is not just typing clever questions. It is interface design for probabilistic systems.

What to practice in prompt engineering

Build prompts that behave like APIs:

System prompts that define role and boundaries
Few-shot examples that demonstrate the format you want
Output formatting so your code can parse results reliably
Clear refusal behavior and safe completion patterns

A practical exercise: make an LLM return JSON that matches a schema (for example: {category, priority, summary, next_action}). Then add automated validation that rejects malformed outputs and triggers a retry with a stricter instruction.

Learn the model layer: OpenAI, Anthropic, and open-source APIs

Paolo lists OpenAI, Anthropic, and Hugging Face. The key is not memorizing endpoints. The key is learning the trade-offs:

Accuracy vs latency
Context window size vs cost
Tool calling support and reliability
Data governance needs (hosted vs self-hosted)

Token management and cost control is a hiring signal

Companies love demos. They hire people who keep the demo affordable. Add cost awareness early:

Log tokens in and out per request
Set max output tokens intentionally
Summarize long histories
Cache stable outputs

By Paolo's end-of-phase goal, you should be able to build an app that takes input, calls a model, and returns structured output. I would add: make it robust under failure (timeouts, rate limits, invalid JSON) because that is where real engineering shows up.

Phase 3 (2-3 months): Production systems, where most candidates drop off

This is the phase that turns "I can call an LLM" into "I can ship an AI feature." Paolo highlights LangChain, RAG, agents, MCP, and LLMOps.

LangChain (or any framework) as plumbing, not the product

Frameworks help you compose prompts, tools, memory, and multi-step logic. But treat them like libraries, not magic. Know what is happening underneath so you can debug:

Where is state stored?
What gets logged?
What happens when a tool call fails?

RAG: give the model your knowledge, not your hopes

Paolo's RAG point is essential: models answer better when they can retrieve relevant context from your docs or databases. In practice, good RAG requires engineering choices:

Chunking strategy (size, overlap, metadata)
Embedding model selection
Vector database and filtering
Retrieval tuning (top-k, reranking)
Answer formatting with citations or evidence

A simple RAG example that impresses recruiters: an internal policy assistant that answers with (a) the decision, (b) the quote from the policy, and (c) a confidence indicator.

Agents: actions, not chat

Paolo writes: "Agents: not just chat. Perform actions, call APIs, update records, trigger workflows." This is where AI becomes business value. If you build an agent, design it like you would design a junior teammate:

Tight tool permissions
Clear guardrails and allowed actions
Audit logs for every step
Human approval for risky operations

MCP and safety with external systems

Paolo mentions MCP as a way to ensure AI interacts safely with external systems like GitHub or Google Docs. Whether you use MCP specifically or another control layer, the idea is consistent: do not give an LLM raw, unlimited power. Implement least privilege, scoped tokens, and policy checks.

LLMOps: treat prompts like code

In production, prompts change, models update, and behavior drifts. LLMOps means:

Prompt versioning
Offline evaluation sets
Monitoring for quality regressions
Cost dashboards
Incident playbooks for model changes

Phase 4: Get hired by proving you can ship

Paolo Perrone's hiring advice is concrete: build two projects that look like real work, then present them well. I agree with the project-first approach because it collapses ambiguity.

Project 1: RAG decision support system

Include the elements Paolo listed: embeddings, semantic search, structured outputs, and confidence scores. To make it stand out:

Add citations to retrieved passages
Add an evaluation script (a small labeled dataset)
Show failure cases and how you mitigated them

Project 2: AI workflow orchestrator

This is highly employable: ingest tickets or emails, classify, prioritize, apply business rules, and trigger actions. Keep it realistic:

A queue (even a simple job runner)
Deterministic business rules wrapped around the model
Tool calls (create Jira ticket, send Slack message, update CRM)
Observability (logs, metrics, tracing if possible)

Presentation matters: clean code, docs, and demos

Paolo says: "Clean code, document everything, make demo videos." Treat your GitHub like a product:

A crisp README with architecture diagram
A 2-4 minute demo video
Setup instructions that work
Example inputs and expected outputs

A simple way to start today (without overplanning)

Paolo ends with urgency: "Don't wait until you feel ready. Pick one phase. Start today. Make mistakes. Iterate."

If you want a practical starting point, pick one deliverable per phase:

Phase 1: a small API client service with solid error handling and tests
Phase 2: a structured-output LLM endpoint with JSON validation and retries
Phase 3: a RAG pipeline with citations and monitoring of cost and latency
Phase 4: two polished repos with demos and a targeted resume

Momentum is the moat. The roadmap works if you treat each phase as something you can ship, not just study.

This blog post expands on a viral LinkedIn post by Paolo Perrone, No BS AI/ML Content | ML Engineer with a Plot Twist 🥷100M+ Views 📝. View the original LinkedIn post →