What is the difference between an AI developer and a machine learning engineer?

An ML engineer typically trains and deploys models. An AI developer, as most companies now use the term, builds products on top of large language models — integration, retrieval, agents, evaluation and cost control. Most product teams need the second. If your problem genuinely needs model training, we will tell you.

Can your AI developers work in our existing codebase?

Yes, that is the normal setup. They work in your repository, follow your conventions and code review, and put AI code next to the feature it powers rather than in a separate service nobody owns.

Which models and frameworks do they use?

Whichever passes your evaluation set at an acceptable cost — usually Claude, GPT or Gemini, through the Vercel AI SDK, LangChain or LangGraph. Models sit behind an interface so switching providers is a config change plus an eval run.

Hire AI Developers

Full-stack engineers who build LLM features into real products — not prompt tinkerers, not research scientists.

Get Matched with a Developer

Most companies looking for "an AI developer" do not need someone to train models. They need an engineer who can wire Claude, GPT or Gemini into an existing product safely: structured outputs that fit the database, retrieval over company data, agents with scoped permissions, evaluation sets that catch regressions, and cost tracking per customer. Our AI developers are full-stack engineers first, which is what makes the AI part ship.

What clients hire our AI engineers for

AI features in an existing SaaS

Extraction, summaries, generated reports and in-app assistants built inside your codebase, behind feature flags, with tests and monitoring.

Agents and workflow automation

Tool-using agents on LangGraph or the Vercel AI SDK that act in your CRM, billing or internal tools — with approval steps where actions are irreversible.

Taking a prototype to production

You have a notebook or demo that works on good days. We add evals, retries, tracing, permissions and a real interface so it works every day.

AI skills we staff for

Claude, OpenAI & Gemini APIs; structured / schema-constrained output

LangChain, LangGraph & Vercel AI SDK

RAG: pgvector, hybrid search, chunking & citation

Evaluation: promptfoo, Langfuse, LLM-as-judge calibrated to human labels

Model Context Protocol (MCP) servers

Production stack: TypeScript, Python / FastAPI, PostgreSQL

When it’s worth bringing in AI expertise

Hire an AI developer when customers are asking for AI features and your team has no LLM production experience, when an AI prototype needs to become something you can sell, or when an existing AI feature is too slow, too expensive or too unreliable to keep.

Flexible Hiring Models

Hourly

Pay only for the hours you need. Ideal for short tasks, fixes, and overflow work.

Dedicated (Monthly)

A full-time developer working only on your product, integrated into your team and tools.

Dedicated Team

A full squad — developers, QA, and a project manager — to own your roadmap end to end.

Need a whole team instead of one engineer? IT staff augmentation or browse every role we staff.

Hiring AI developers — FAQs

Discuss your project

Tell us what you are building or what needs fixing. You will hear back from an engineer — not a sales rep — within one business day, usually with questions or a rough approach.

Your enquiry goes straight to our engineering team — the people who will scope and build your project, not a sales desk.

sales@dablewdevs.com Book a free 30-min call