Everyone is an "AI developer" today: the person who connects to ChatGPT through an API, the one who writes prompts, the one who installs a plugin. A real AI developer builds systems that work in production - LLM pipelines, RAG, fine-tuning, AI agents and integrations that return measurable business value. That's not the same thing.
What is an AI developer - and how is it different from using ChatGPT?
An AI developer is a developer who integrates large language models (LLMs), computer vision models and ML systems into real products and business processes. The job isn't just "sending a prompt to an API" - it's designing the architecture and managing latency, costs, data security and the reliability of the system.
An AI agent is usually one layer inside a wider process. I wrote about the connections around it - forms, CRM, invoices and WhatsApp - in the guide to business automation.
| A professional AI developer | Someone who uses ChatGPT |
|---|---|
| Writes code that brings together an LLM, a database, APIs and business logic | Talks to ChatGPT by hand |
| Builds RAG over the company's documents | Uploads a PDF to ChatGPT Plus |
| Manages token costs, caching and fallback models | Has no idea what a query costs |
| Writes evaluations and tests for hallucinations | Believes every answer |
| Deploys to production with monitoring | The POC never goes live |
| Knows fine-tuning, LoRA and embeddings | Prompt engineering only |
What an AI developer actually does - from RAG to AI agents
In practice the work falls into six areas:
Connecting an LLM to company documents, a database or an internal wiki. The user asks, the system retrieves the relevant information and generates an answer based on facts - not a hallucination. A typical stack: OpenAI Embeddings, Pinecone or pgvector, LangChain.
A system that receives a task, decides which tools to use and carries it out step by step: sends an email, updates the CRM, calls an external API, produces a report. LangGraph, CrewAI, AutoGen - with a human in the loop where needed.
Retraining a base model on the organization's own data. It fits when you need a specific tone, unique terminology or a closed field of knowledge. Not every project needs fine-tuning - sometimes prompt engineering is enough, and part of the job is knowing when.
Extracting data from PDFs, contracts, invoices and forms. Automatic classification of requests, support tagging, sentiment analysis of customer reviews. It saves dozens of hours of manual work every week.
Not a "ChatGPT plugin" - a chatbot connected to the organization's data that keeps history, knows the product and can complete actions. Typical uses: customer support, an HR bot, an internal search engine.
Docker, FastAPI, token cost tracking, rate limiting, A/B testing between models, conversation logging to catch problems. In my projects this is most of the work: it's what separates a POC that runs on a laptop from a product customers actually use.
The technologies a professional AI developer knows
| Layer | Tools | When |
|---|---|---|
| LLM APIs | OpenAI GPT, Anthropic Claude, Google Gemini, Meta Llama | The base of every project |
| Orchestration | LangChain, LangGraph, LlamaIndex | RAG and agents |
| Vector DB | Pinecone, Weaviate, pgvector, Chroma | Semantic search, RAG |
| Fine-tuning | OpenAI fine-tuning, Hugging Face, LoRA, QLoRA | A model adapted to your domain |
| Agents | LangGraph, CrewAI, AutoGen, n8n | Complex automation |
| Deployment | FastAPI, Docker, AWS/GCP, Modal | Production |
When AI is worth the investment - and when it isn't
This is one of the most important questions, and most AI websites never answer it honestly. A good AI developer will tell you when not to build AI:
| Situation | AI is worth it | AI is not worth it |
|---|---|---|
| Processing text and documents | A very good fit | - |
| Simple business logic (if/else) | - | Regular code is enough |
| Questions about an internal knowledge base | RAG solves it | - |
| High-volume customer support | A clear ROI | - |
| Decisions that need 100% accuracy | - | Hallucination risk |
| Automating a repetitive process | Payback in 3-6 months | - |
| Sensitive data that must not go to GPT | A local model (Ollama) | - |
AI projects that fail don't fail because of the technology - they fail because nobody asked "what business problem are we solving?". I've seen businesses spend a serious budget on an AI implementation that solved a problem a spreadsheet could have handled. The principle I work by: AI is a tool, not a goal. It's worth it when it solves a problem that simpler tools couldn't - and not before.
AI developer for business - common project types
AI projects I've built for businesses in 2024-2026 - the numbers are from those projects, not industry averages:
- RAG over internal knowledge - lawyers who can ask questions about 10,000 contracts. An answer in 3 seconds instead of 3 hours of searching.
- An AI agent for customer support - answering 80% of requests without a human agent, with smart escalation to people.
- A recommendation engine - user behavior analysis plus an LLM to personalize content and products.
- Back-office automation - invoices, contracts, reports: what took 4 hours takes 4 minutes.
- Claude or GPT integration in WordPress - content creation, SEO, automatic FAQ answers.
The career behind the code
I didn't start with AI - I started with systems. At William Hill Online I was Tech Lead on systems serving millions of users. At Mars Media Group and BuyMe I built systems at real scale. When I added AI after 20 years of building production systems, one thing was already clear to me: AI doesn't replace solid software - it sits on top of it. An AI system built on a weak foundation will fall.
The full profile: LinkedIn - Vadim Leviev
How much does it cost to hire an AI developer?
| Type of service | Price | Typical time |
|---|---|---|
| AI consultation - scoping and stack choice | $690 | 2-3 hours |
| Basic RAG over documents | from $2,200 | 2-3 weeks |
| Chatbot connected to company data | from $3,300 | 3-5 weeks |
| Complex AI agent with tools | from $5,600 | 6-10 weeks |
| Fine-tuning and deployment | from $4,200 | 4-8 weeks |
| Monthly retainer (maintenance and development) | $1,100/month | Ongoing |
Not sure yet what to build? Start with AI consulting. Rolling AI out across a whole company, with a pilot, security and staff training? See AI implementation.
If you or your team spend more than an hour a week on repetitive tasks - searching documents, answering the same questions, summarizing and writing - there's a good chance AI will pay for itself within months, not years. The question isn't whether, but how long you can afford to wait before a competitor does it right.
AI developer: frequently asked questions
What is the difference between an AI developer and a data scientist?
A data scientist focuses on data analysis, statistics and building ML models. An AI developer focuses on integrating existing models (LLMs, APIs) into products and systems - more software development, less research. Most business projects need an AI developer, not a data scientist.
Do you need Python to build an AI system?
Python is the dominant language for AI development - LangChain, LlamaIndex and Hugging Face are all Python-first. But the API calls themselves can be made from any language. A production system usually combines a Python backend with a frontend language of your choice.
How long does it take to build an AI chatbot for a business?
A basic chatbot connected to an FAQ takes one to two weeks. An AI chatbot with RAG over company documents takes 3-5 weeks. A chatbot with CRM and ERP integrations that can also take actions takes 6-12 weeks. It depends on how complex the data is and how many integrations there are.
Does my company data go to OpenAI?
With the standard API, yes - the data passes through OpenAI's servers. For sensitive data there are three options: (1) OpenAI Zero Data Retention with a business agreement, (2) Azure OpenAI, served on Microsoft infrastructure without data sharing, (3) a local model (Llama through Ollama) that runs entirely on your own infrastructure.
What is RAG and why does it matter?
RAG (Retrieval Augmented Generation) is the way to connect an LLM to your own information without retraining it. Instead of the model knowing only what was on the internet up to its training date, RAG retrieves the relevant documents in real time and feeds them in as context. The result: answers based on your data, not hallucinations.
Will AI replace developers?
AI replaces tasks, not professions. In my own projects, a developer who uses AI as an assistant gets about three times as much done - one who does not gets pushed aside. The right question is not whether AI will replace developers, but what to learn in order to work alongside it.
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