Hire AI Developers

What an AI developer does on your team

An AI developer builds product features on top of large language models and connects them to your data and systems. The role is closer to a senior software engineer than to a researcher. Most of the work is software: APIs, data pipelines, testing and deployment.

On a typical team, an Arcesso AI developer works on four kinds of tasks:

• LLM apps: features such as chat, summaries, document extraction and drafting, built on OpenAI, Anthropic Claude, Google Gemini or open-weight models.
• RAG: retrieval that pulls the right passages from your documents and database, so answers are grounded in your data and cite their sources.
• Agents: workflows where the model calls your tools and APIs to finish a task, with permissions, logs and a human approval step.
• Evals: test sets and scoring that show whether a change made the system better or worse, before it reaches users.

How we vet AI developers

Arcesso vets AI developers on real AI work, not on quiz questions or certificates. Our own engineers run the interviews, and they build AI agents and voice agents for clients every week, so they know what good work looks like.

Every candidate goes through the same steps:

• A review of AI work they have shipped to production, with questions about what broke and how they fixed it.
• A practical task: build a small RAG feature over a sample document set and write an eval that measures its accuracy.
• A design discussion on cost, latency and failure modes, such as what the system should do when the model is wrong or slow.
• A code review of their task with one of our engineers, checking tests, structure and clarity.
• A check on communication, since AI work needs clear written trade-offs for non-technical stakeholders.

AI developer vs ML engineer vs data scientist

AI developers, ML engineers and data scientists solve different problems, and hiring the wrong one wastes months. Most companies adding AI to a product today need an AI developer, because the models already exist and the work is connecting them to the product safely.

Hire an ML engineer if you need to train or fine-tune your own models at scale. Hire a data scientist if the main question is analysis and forecasting. Arcesso places AI developers and will tell you on the first call if you need a different role.

The roles also combine well. An AI developer can ship the first version on a hosted model, and the eval data they collect becomes the training set if you later decide a custom or fine-tuned model is worth the cost.

Models and tools our AI developers work with

Arcesso AI developers work with the models and tools we build on for our own clients. That includes OpenAI, Anthropic Claude, Google Gemini and open-weight models for language tasks, and Vapi, Retell, LiveKit, ElevenLabs and Twilio for voice agents.

On the software side, they build with Python and Node backends, Next.js and React front ends, and Postgres for data and vector search. They deploy on AWS, Vercel and Supabase, and use n8n and Make when a workflow is better as an automation than as custom code.

Arcesso already runs AI voice receptionists, AI appointment booking and WhatsApp AI agents for service businesses, startups and government portals. The developers we place come from that same practice. We do not claim partner status with any model provider; these are simply the tools we use and know well.

How AI developers handle your data

Arcesso AI developers follow a written data policy that says what goes to which model and how long it is kept. You can read it on our AI use page before you share anything. A mutual NDA is in place within 24 hours of asking.

In practice, that means a few fixed habits. Developers use API settings that keep your data out of model training where the provider offers it. They send models only the data a task needs, not whole databases. They log every agent action, and any action that moves money, sends messages to customers or changes records goes through a human approval step.

If your data cannot leave your cloud, our developers can work with open-weight models hosted in your own AWS account. We will discuss the trade-offs in cost, accuracy and upkeep on the first call.

When hiring an AI developer is the wrong move

Hiring an AI developer is the wrong move if you do not yet know which problem AI should solve. An engineer without a clear use case tends to build demos that never reach users. In that case, start with an AI readiness assessment, which ranks your use cases by value and risk.

It is also the wrong move if you need a single, defined AI feature with a deadline. A fixed-price AI agent project caps the cost and puts delivery risk on Arcesso instead of on your team.

Finally, an AI developer cannot fix messy or missing data alone. If the documents or records the model needs are scattered, outdated or locked in systems without APIs, plan for that data work first. We will tell you on the discovery call if that is the real bottleneck.