Hire AI developers
Easital Technologies Ltd. provides dedicated AI engineers and teams who work in your repository, your backlog and your review process. They build generative AI features, agents, chatbots and LLM systems. You direct the work day to day, and Easital stays responsible for the people: their employment, their management and a replacement if one is needed.
You add engineers to your team under a services contract. They work on your product, take direction from your technical lead and remain on Easital’s staff, so you add capacity without recruiting or payroll.
An AI developer, as the term is used here, is a software engineer who builds with language models: prompts and context, retrieval, tool calling, evaluation, deployment and running cost. The same person also writes the ordinary backend and frontend code that an AI feature lives in. That second half matters, because a model call is a small part of a product.
This arrangement is staff augmentation. It differs from project outsourcing, where a supplier takes a defined result and manages the work itself. Easital offers both, and the choice depends mostly on whether you have someone to direct engineers.
Staff augmentation adds people to your team and you manage the work. Outsourcing hands a defined outcome to a supplier who manages the work. The first buys capacity and skills, the second buys a result.
| Staff augmentation | Project outsourcing | |
|---|---|---|
| Who manages daily work | Your technical lead | The supplier’s project manager |
| What you buy | Engineers’ time and skills | A defined deliverable |
| Who sets priorities | You, week by week | The agreed scope; changes go through change requests |
| What you need in-house | Someone to direct and review the work | A product owner to accept the result |
| Main risk you carry | Direction: the work is only as good as its priorities | Scope: it has to be right before work starts |
| Where knowledge ends up | In your repository and your team’s process | It depends on the handover |
| Fits best | A changing roadmap and an existing team that needs skills or capacity | A well-defined project and no team to run it |
For a scoped project, see for example AI agent development. For the wider question of working with a remote engineering company, see offshore software development.
Six roles cover most AI product work. One engineer often fills more than one of them, and a team combines them.
Features that generate, summarize, extract or transform text and other content, with structured outputs and tests for quality. Related: generative AI development.
Agents that plan steps and call tools, with permissions, step limits, tracing and human approval where an action cannot be undone.
Retrieval over your documents, model selection, fine-tuning, cost and latency work, and models that run on your own servers. Related: LLM development.
Support and sales assistants for web and messaging channels that answer from your knowledge base and hand over to a person with the context. Related: AI chatbot development.
Phone agents: speech-to-text, text-to-speech, telephony, call transfer and the handling of interruptions. Related: voice AI agent development.
The backend, frontend, billing and infrastructure around the AI feature, for teams that need the whole product built and not only the model integration.
An engagement runs in seven steps, from a description of the role to a handover at the end. You choose the people, and nothing starts before the contract terms are agreed.
The role or roles, your stack, the stage of the product, your time zone and when you want to start.
We propose specific people. You interview them as you would interview a hire, including a technical exercise if you use one, and you decide.
Ownership of code and other work, confidentiality, security practices, working hours and overlap, notice, and what happens if a person has to be replaced.
You grant access to the repository, the issue tracker and your chat through your own accounts. The engineer reads your documentation, sets up the project and ships a first small change through your review process.
Your stand-ups, your sprints and your pull-request reviews. Engineers work with agreed overlap with your time zone and write up progress and open questions for the hours you do not share.
Your lead and an Easital manager check in at an agreed interval on quality and fit. You can add people, reduce the team or ask for a replacement under the contract terms.
At the end, open work is documented, knowledge is passed to your team and all access is removed.
The way to judge AI talent is by systems that are live. These four are, and each can be opened and tried.
Client project
Built by Easital for a client in the United States
Real-time voice engineering: a platform whose agents hold phone conversations using speech recognition, a language model and synthesized speech, connected to carriers and to customers’ own phone systems.
Easital product
Built and run by Easital
Agentic AI: an AI chat and an agentic code editor that work inside a cloud sandbox, with live preview and one-click deployment.
Easital product
Built and run by Easital
Generative AI in a media pipeline: a Chrome extension and web app that turn one screen recording into a narrated how-to video with captions and a written guide.
Easital product
Built and run by Easital
Applied automation: one dashboard from which an online store builds its website, creates content, runs marketing and answers customers with AI.
They work as members of your team who happen to be employed by Easital. The points below are the ones buyers ask about, and each is written into the contract and not left to goodwill.
Engineers work with agreed overlap with your time zone. The overlap window is set before the start and covers your regular meetings.
The contract states who owns the code and everything else produced. Work is committed to your repository from the first day.
If an engineer is not the right fit, or becomes unavailable, Easital proposes a replacement and manages the handover. Notice and terms are in the contract.
Agreed in the contract: confidentiality, access through your accounts with the minimum permissions needed, the handling of secrets and customer data, and removal of access at the end.
Check for evidence that a candidate has taken a language-model feature past the demo: live systems, a method for testing model output, and attention to cost.
Calling a model API is easy, and the difficulty lies in the output that is nearly right. In the Stack Overflow Developer Survey 2025, 66% of developers named “AI solutions that are almost right, but not quite” as their biggest frustration with AI tools. An AI engineer’s job is to close that gap in your product. These questions show whether a candidate can:
An engineer placed in your team works in your stack and with the providers you have chosen. Skills are listed by category.
Easital engineers work across the major commercial model providers and with open-weight models, so they can build on the one you already use or help you compare the alternatives.
Commercial and open speech engines, the main telephony carriers and existing phone systems.
There are three options. The first two are staff augmentation, and the third is for teams that would be better served by a project.
A single AI engineer who joins your team and reports to your technical lead.
Best for: a team that lacks one skill, such as LLM evaluation, retrieval or voice.
Several engineers with an Easital lead who coordinates them, working from your backlog and inside your review process.
Best for: a product area you want staffed from design to release.
Easital takes a defined result, manages the work and hands it over. Start from the service that matches it, for example AI automation services.
Best for: companies with a clear goal and no technical lead to direct engineers.
Describe the role, your stack and your time zone. Easital proposes specific engineers, you interview them and choose, and the contract is agreed before work starts. The engineer then gets access through your accounts and ships a first small change through your review process.
With staff augmentation, engineers join your team and you manage their work. With outsourcing, a supplier takes responsibility for a defined result and manages the work itself. Staff augmentation suits a changing roadmap and a team that can direct engineers. Outsourcing suits a well-defined project.
Yes. Say which kind of work you need: generative AI features, agents that call tools, chatbots, voice agents, or retrieval and model work. We propose engineers whose experience matches it, and you confirm the match in the interview.
Ownership is set out in the contract before work starts. The engineers commit to your repository under your accounts from the first day, so the code is in your hands throughout the engagement.
They work with agreed overlap with your time zone. The overlap window is fixed before the start so that your stand-ups and reviews fall inside it, and progress is written up for the hours that are not shared.
Tell Easital. We propose a replacement, you interview that person, and Easital manages the handover between the two. The notice period and the terms of a replacement are part of the contract.
Through terms agreed in the contract: confidentiality, access granted through your accounts with the minimum permissions the work needs, rules for handling secrets and customer data, and removal of all access when the engagement ends. Tell us which of your own security requirements apply, and they are written in.
Describe the role, your stack and your time zone. We write back with questions and, where we have a match, the engineers we would propose.
Easital is an AI and SaaS engineering company that takes AI software to production, and runs AI products of its own. Founded in 2019.