[ Generative AI, on demand ]

Hire generative AI developers and LLM engineers, matched in minutes

Q: Do I need to fine-tune a model to build a generative AI product?

Usually not. Most 2026 generative AI products are built with a strong base model plus retrieval over your own data and good prompting, which is cheaper and easier to maintain than fine-tuning. A vetted developer will tell you honestly when retrieval is enough and when fine-tuning actually earns its cost.

Describe the generative AI build you need in plain language: a RAG assistant over your docs, a customer-facing chatbot, a content or code copilot, an LLM pipeline. Botgigs matches you to a vetted generative AI developer or LLM engineer who has shipped that exact kind of system, or a ready-made agent when one already solves it. Real production experience, without the agency retainer.

Free hire brief · No card required · Vetted LLM and generative AI specialists

[ HIRE-BRIEF GENERATOR ]

demo · free · no signup · up to 10 briefs per session

describe the automation

hire

stack

brief.json

[ pre-generated sample ]

best-effort AI estimate, not a quote or a match

job

ticket_01

scope of work

who to hire

screen for

effort estimate

questions to ask your hire

Like the brief? Get matched to the right specialist when we launch.

01 / what they build

What a generative AI developer actually builds

Generative AI work in 2026 is mostly building on top of strong base models, not training from scratch. Botgigs vets for the specific skills below, so you hire the developer whose shipped work matches your build instead of paying a bench for time you never use.

[ rag ]

RAG and knowledge assistants

Retrieval over your docs, help center and databases so the model answers from your truth with citations, not a generic guess. The most common and highest-ROI build.

[ chatbots ]

Chatbots and copilots

Customer-facing and internal assistants that hold context, call your tools, and hand off to a human cleanly when confidence drops.

[ content ]

Content and code generation

On-brand content pipelines, code assistants and document drafting wired into your systems, with the review loops that keep output trustworthy.

[ agents ]

Agent workflows

Multi-step agents that plan, call tools and act across your stack, with the guardrails and evaluation that keep them from going off the rails.

[ finetune ]

Fine-tuning and evaluation

Fine-tuning, prompt optimization and a real eval harness, used only where they earn their cost, so you know when the system is right and when it is wrong.

[ llmops ]

LLM pipelines and LLMOps

The plumbing that makes a generative feature production software: streaming, caching, cost controls, monitoring and safe deployment.

02 / why botgigs

Production generative AI skill, without the agency markup

01

Hire the builder, not the bench

You work directly with the generative AI developer building your product, so there is no account manager between you and the person who understands your data, and no agency margin on their rate.

02

Scoped before you spend

The hire brief turns your idea into deliverables, a build approach and an honest effort band up front, so a generative AI project cannot stall in a paid discovery phase. See how hiring works.

03

Buy the agent when it fits

Some generative AI problems are now finished products. When one matches, you hire a working agent instead of paying to build from scratch, or build an AI agent to spec when it does not.

04

Vetted on shipped LLM work

The bar is a generative feature that ran in production: what it did, how they evaluated it, and how they controlled cost and hallucination. Every specialist on the marketplace is screened on evidence, not buzzwords. For custom or private model builds, see the LLM development company route.

03 / compare the routes

Botgigs vs an AI agency vs an in-house generative AI hire

Three honest ways to get generative AI built. The US figures below are typical 2026 market ranges, not quotes. Which one fits depends on scope, how much ongoing LLM work there is, and your compliance needs.

Route	Time to start	Typical US cost	Best for
Botgigs developer or agent	Days	$80 to $250/hr, or roughly $3,000 to $20,000 per scoped build	One clear generative AI feature, fast, without agency margin
Generative AI agency	Weeks (discovery first)	$15,000 to $40,000/month retainer, or $50,000+ per project	Large, multi-team programs needing managed delivery
In-house generative AI hire	Months to hire	$120,000 to $250,000 per year, plus 25 to 40% overhead	Generative AI as a core, always-on part of the product

Not sure whether you need a builder or a strategist first? Read AI consultant vs AI developer, or compare the wider AI development services route.

04 / how it works

From idea to the right generative AI hire, in minutes

step_01

Describe the build

What you want the model to do, the data it should use, and the systems it must reach. The AI turns it into scope: approach, deliverables and the LLM skills to screen for.

step_02

Get scope and matches

A vetted generative AI developer who has shipped this kind of build, or a ready-made agent if one already fits. No proposal spam, no bidding war.

step_03

Hire and ship

Agree the milestones from the brief and start. Milestone escrow and consistent vetting are part of the planned launch scope.

05 / questions

Generative AI hiring questions, answered

What does a generative AI developer do?

A generative AI developer builds software on top of large language models: RAG apps over your documents, chatbots and copilots, content and code generation, and agent workflows that call your tools. They own prompt design, retrieval, evaluation and deployment, working with frameworks like LangChain, LlamaIndex, Hugging Face and the OpenAI and Anthropic APIs.

How much does it cost to hire a generative AI developer?

In the US, freelance generative AI developers typically charge $80 to $250 per hour, with senior LLM specialists at the top of the band. A single scoped build, such as a RAG assistant or a customer chatbot, usually runs $3,000 to $20,000, well below a full-time hire at $120,000 to $250,000 a year plus overhead.

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

A generative AI developer builds on top of existing large language models: prompting, retrieval, tool calling and agent design. A machine learning engineer more often trains and deploys custom predictive models from your own data. Many builds today need mostly the generative AI skill set, with an ML engineer added only when a custom trained model is genuinely required.

Do I need to fine-tune a model to build a generative AI product?

Usually not. Most 2026 generative AI products use a strong base model plus retrieval over your own data and good prompting, which is cheaper and easier to maintain than fine-tuning. A vetted developer will tell you honestly when retrieval is enough and when fine-tuning actually earns its cost.

Which is better, hiring a freelance generative AI developer or an agency?

For one well-defined build, a single vetted freelance developer is faster and cheaper because you skip the agency margin and discovery phase. Bring in an agency when the work spans several teams and needs managed, long-term delivery. Botgigs lets you start with the specialist and scale up only when scope genuinely grows.

How do you vet a generative AI developer?

Look past framework name-drops and ask for a generative feature they shipped to production: what it did, how they measured quality, how they controlled cost, and what they did about hallucinations. Botgigs screens on exactly that evidence, so you are matched on shipped work rather than a keyword-stuffed profile.