A year ago, if you searched for “AI Product Engineer” on LinkedIn, you would find a handful of job listings with wildly different descriptions. Today, the role is everywhere, but the confusion hasn’t cleared up much. I’ve been working as a Senior AI Product Engineer for a while now, and I still get asked at meetups: “So, do you train models?”

No. I don’t train models. But I ship products that are powered by them.

Let me break down what this role actually looks like from the inside.

The Role Doesn’t Have a Clear Definition Yet

Here’s the honest truth: AI Product Engineer means different things at different companies. At some places, it’s a glorified prompt engineer. At others, it’s basically a full-stack engineer who happens to call the Claude API. And at a few companies, it’s something genuinely new.

The core of the role, as I’ve experienced it, is this: you take the raw capabilities of large language models and turn them into products that real people can use. That means you’re not just calling an API and dumping the response on screen. You’re designing the pipeline, handling edge cases, building fallback strategies for when the model hallucinates, and creating evaluation systems to make sure the output quality stays high over time.

It sits at the intersection of product thinking and AI engineering. You need to understand what users actually need, and you need to understand enough about how LLMs work to build reliable systems around them.

How It Differs from ML Engineer

This is the question I get most often. The short answer: ML engineers build and train models. AI product engineers use models to build products.

An ML engineer spends their day working on training pipelines, datasets, loss functions, and model architecture. They care about perplexity scores and training compute. They often have a research background, sometimes a PhD.

I spend my day thinking about how to get Claude to reliably extract structured data from messy customer emails, how to design a RAG pipeline that doesn’t retrieve irrelevant documents, and how to build an evaluation suite that catches regressions before they reach users.

You don’t need a PhD for this role. You need strong engineering fundamentals and a deep practical understanding of how LLMs behave. Knowing that temperature affects randomness matters more than knowing the math behind attention mechanisms. Understanding token limits and context window management matters more than knowing how transformers are trained.

How It Differs from Traditional Software Engineer

On the other side, traditional software engineers sometimes think this role is just “software engineering with an API call.” It’s not.

The fundamental difference is that a significant part of your system is non-deterministic. When you call a traditional API, you send a request and get a predictable response. When you call an LLM, you send a prompt and get a response that might be different every time. It might hallucinate. It might ignore your instructions. It might format the output differently than you expected.

This changes everything about how you architect, test, and monitor your systems. You need to understand prompt engineering deeply, not just “write a good prompt” but understanding system prompts, few-shot examples, chain-of-thought reasoning, structured outputs, and tool use. You need to understand embeddings and vector search for RAG systems. You need to know how to build agents that can reliably complete multi-step tasks. And you need evaluation frameworks because you can’t just write unit tests that check for exact equality.

Core Skills You Actually Need

From my day-to-day work, here are the skills that matter most:

Prompt engineering is the foundation. Not just writing prompts, but designing prompt systems. Knowing when to use few-shot examples versus detailed instructions. Understanding how to get structured JSON output reliably. Knowing the differences between how Claude, GPT, and DeepSeek handle the same prompt.

API integration across multiple LLM providers. You need to be comfortable working with the Claude API, OpenAI API, and increasingly with local models through Ollama. Each has different strengths, and production systems often use multiple models for different tasks.

RAG pipelines are in almost every AI product I’ve worked on. You need to understand chunking strategies, embedding models, vector databases, retrieval tuning, and re-ranking. I wrote a full guide on building RAG that actually works if you want the technical details. A bad RAG pipeline makes your entire product look broken even if the LLM is perfect.

Agent orchestration is becoming a bigger part of the job. Building systems where an LLM can use tools, make decisions, and complete multi-step workflows — I covered the fundamentals in my post on building your first AI agent. MCP (Model Context Protocol) has been a game changer here for standardizing how agents interact with external tools.

Evaluation and testing is probably the most underrated skill. You need to build test suites that can assess AI output quality at scale. This includes automated evaluation using LLMs as judges, human evaluation workflows, and regression testing for prompt changes.

Traditional full-stack skills still matter enormously. I write Go for backend services, Python for AI pipelines, React/Next.js for frontends, and Flutter for mobile. The AI part is maybe 30-40% of the job. The rest is solid software engineering.

A Typical Day

My mornings usually start with checking pipeline metrics. How did our AI features perform overnight? Did the hallucination rate spike? Are response times within acceptable bounds? Last week I caught an issue where a prompt change had degraded the quality of our entity extraction by about 15%. Caught it from the eval dashboard before any user reported it.

Mid-morning I might be debugging a specific failure case. A user reported that our AI assistant recommended something completely wrong. I trace it back through the pipeline: was it a retrieval issue (wrong documents pulled from the vector DB), a prompt issue (the instructions weren’t clear enough for this edge case), or a model issue (the model genuinely couldn’t handle this type of reasoning)? Each root cause has a different fix.

Afternoons often involve building new features. Recently I spent a week building an MCP server that lets our AI assistant interact with our internal project management tool. That means defining the tool schemas, handling authentication, building error handling for when the tool calls fail, and testing the entire flow end-to-end.

Late afternoon is usually for evaluation work. I run our prompt test suites against proposed changes, review the results, and decide if the changes are safe to ship. This is surprisingly similar to code review, you’re looking at diffs and assessing risk, but the “code” is prompts and the “tests” are AI-judged evaluations.

The Tech Stack

Here’s what I actually use on a regular basis:

LLM APIs: Claude is my primary model for complex reasoning tasks. DeepSeek for cost-effective bulk processing. GPT for specific tasks where it performs better.

Vector databases: pgvector for most projects because it lets us keep everything in PostgreSQL instead of adding another database to the stack. Pinecone when we need managed scaling.

Embedding models: We run embedding models locally through Ollama for sensitive data that can’t leave our infrastructure. For everything else, we use API-based embedding models.

Frameworks: I’ll be honest, I’ve moved away from heavy frameworks like LangChain for most things. For simple LLM calls, the SDKs are enough. For complex agent workflows, I prefer building lightweight orchestration layers that I fully understand and can debug.

Backend: Go for high-performance services, Python for AI-specific pipelines and scripts. The combination works well because Go handles the serving layer and Python handles the ML-adjacent processing.

Frontend: Next.js for web applications, Flutter for mobile. Both integrate well with streaming responses from LLM APIs, which matters a lot for user experience.

How to Become One

If you’re a software engineer looking to move into this role, here’s my honest advice:

Start building AI products today. Don’t wait for a job title change. Take a side project and add AI features to it. Build a RAG chatbot over your own documents. Create a CLI tool that uses Claude to do something useful. The best way to learn is to ship.

Learn prompt engineering properly. Read the Claude documentation, the OpenAI cookbook, and Anthropic’s prompt engineering guides. Then practice. A lot. The gap between someone who “knows how to prompt” and someone who can reliably get production-quality output is enormous.

Understand embeddings and vector search. Build a simple semantic search system from scratch. Understand how chunking strategies affect retrieval quality. This knowledge is foundational to almost every AI product.

Build an agent. Create something that uses tool calling, makes decisions, and completes multi-step tasks. Build an MCP server. This is where the industry is heading, and hands-on experience matters.

Don’t abandon your existing skills. Your backend, frontend, and infrastructure skills are what make you valuable. Anyone can call an API. Not everyone can build a production system around it that handles errors gracefully, scales properly, and gives users a good experience.

Why This Role Matters

Here’s what I believe deeply: AI is only valuable when it reaches users through well-built products. The most powerful model in the world is useless if it’s sitting behind a bad interface, returning hallucinated responses, or taking 30 seconds to respond.

Someone needs to bridge the gap between what models can do and what users need. Someone needs to handle the messy reality of non-deterministic systems in production. Someone needs to care about both the AI quality and the product experience.

That’s what an AI Product Engineer does. The role is new, the definition is still forming, and the tools change every few months. But the core mission is clear: take AI capabilities and turn them into products that actually work for people.

If that sounds exciting to you, there’s never been a better time to start.