No published specialists with this skill yet.
Describe your project above so we can assess the requirements, or apply as a AI Native Developer developer. Specialist availability will need to be confirmed.
AI native developer is an editorial description for a software engineer who uses AI throughout the delivery process and can also build products whose workflows depend on language models, agents or intelligent automation. It is not a regulated title or a guarantee of a particular toolset.
When you hire an AI native developer, describe the business decision or user task that should improve. A good brief names the data the system may access, the actions it may take, where a person must approve a result and how an incorrect answer should be detected.
AI tools can accelerate scaffolding, debugging, test generation and developer automation tools, but generated code still needs review. An AI native developer should understand the existing architecture, keep changes reproducible and verify important behaviour with tests rather than treating a plausible response as evidence.
Ask how the developer will protect secrets, review generated dependencies, handle private source code and keep the repository understandable to the rest of your team. The useful advantage is a faster feedback loop that remains accountable to acceptance criteria.
An AI agent developer may connect a model to tools such as a database query, a ticket system or an internal API. The production boundary matters: each tool needs a narrow contract, authorization, input validation, timeouts and a clear record of what happened. A human approval step may be required before an irreversible action.
For RAG and other LLM applications, scope the document sources, ingestion process, chunking, retrieval, citations and evaluation examples. A demo that answers one question is not enough to establish retrieval quality, privacy or safe behaviour across the cases your customers will submit.
A generative AI developer can help move a prototype toward a service that your team can operate. The work may include a backend API, a queue for long-running jobs, observability, feedback capture and a release process that allows prompts and model versions to be reviewed safely.
Do not evaluate the result only by how impressive a handful of answers look. Agree what quality means for your domain, which failures are unacceptable, how users can correct the system and who owns the data, prompts, evaluation set and infrastructure after handover.
Include the current product state, the target users, representative inputs, systems that must be connected and the actions the AI may or may not perform. Say whether you need an AI agent, a retrieval feature, an assistant inside an existing product or AI-assisted development of a conventional application.
Request a short technical plan with risks, testable milestones and a handover outline. Rates, availability and relevant delivery experience must be confirmed with the individual developer; this page does not make claims about a specific candidate.
It is an editorial term for a software developer who uses AI tools throughout delivery and can build AI-first product workflows. It is not a formal certification, so evaluate the person's relevant code, architecture decisions and production practices.
Choose by the work. An LLM developer may focus on model integrations and retrieval, while an AI native developer may also cover the surrounding product and use AI-assisted engineering. A project can need both capabilities in one person or across a team.
Yes, if the scope includes authenticated tools, least-privilege access, validation, logging and a safe approval path. Do not share credentials in a public brief; arrange private access after agreeing the scope.
Ask for representative evaluation cases, failure handling, privacy boundaries, cost and latency assumptions, monitoring and a rollback plan. The exact checks depend on the decisions the application supports.
There is no single rate for AI work. Request an estimate that separates discovery, application engineering, model or provider costs, evaluation, deployment and handover. Confirm the developer's rate directly.
