Hire AI Developers: LLM, Agents and RAG Engineers
Senior engineers who have already shipped agents, RAG systems and self-hosted LLMs to production, and who put evals around anything your customers touch.
- Clutch 5.0, 53 verified reviews
- Code, prompts and evals stay yours
- EU company based in Malta
- Proof of concept in days, MVP in weeks
What do AI developers build?
When you hire AI developers from Valletta, you get senior engineers who build products on top of foundation models: LLM applications, retrieval augmented generation (RAG) pipelines and agents that complete multi-step work. They design prompts and retrieval, write evals, integrate OpenAI and Azure AI into your stack, and fine-tune models where it pays off.
Where we stop: we do not train foundation models and we do not run ML research. Our ceiling is LoRA fine-tuning, applied computer vision and model benchmarking. If your project needs research, we say so on the first call.
- LLM features inside an existing product, connected to your data and permissions
- RAG systems over your documents, with retrieval quality measured and monitored
- Agents that act in Slack, email, calendars and CRMs, with human approval before anything is sent
- Self-hosted LLMs for data that cannot leave your infrastructure
- Evals and regression checks that block a release when answers get worse
What do you get when you hire an AI engineer from Valletta?
You get a vetted senior AI engineer plus the parts a production AI feature needs. That means evals and safety checks before outputs reach a customer, integration with your existing stack and data, and DevOps support for deployment and monitoring when you choose a dedicated team. Code, prompts and evaluation sets stay yours.
Vetted senior engineers
Screened for LLM application work: prompt design, retrieval, agent workflows, fine-tuning and evals. We interview and test 15 to 18 engineers per placement.
Integration with your stack
OpenAI, Azure AI or a self-hosted model, connected to your data and your existing services, not a chatbot bolted on the side.
Evals and safety checks
Outputs stay accurate, measurable and reviewable. Evals run on every change to prompts or models.
Code and IP that stay yours
Prompts, evaluation sets, pipelines and infrastructure code are documented so your own team can run them.
Hire an AI engineer or a machine learning engineer?
Hire an AI engineer when the product is built on top of an existing foundation model: prompts, retrieval, agents and evals. Hire a machine learning engineer when you need custom models for classification, forecasting or computer vision, and the MLOps around them. Most production AI features need some of both, so our dedicated teams can combine them.
| AI engineer | Machine learning engineer | |
|---|---|---|
| Builds on | Existing foundation models through APIs or self-hosting | Custom models trained on your data |
| Typical work | LLM apps, RAG pipelines, agents, evals, integrations | Classification, forecasting, computer vision, MLOps |
| What Valletta covers | Full scope, from prototype to production | Applied work up to LoRA fine-tuning, applied computer vision and model benchmarking |
| What Valletta does not do | Foundation model training | ML research |
How does hiring AI developers work?
You describe the use case, the data it touches and the stack it has to live in. We say whether it is a one-engineer job or a team job and send a shortlist of vetted engineers. You interview them and decide. Dedicated teams start with an evaluation sprint, and evals run on every change after that.
Brief
The use case, the data it touches and the stack it has to live in. We say whether it needs one engineer or a team.
Shortlist and interviews
You interview vetted senior candidates and keep full control of scope and direction.
Evaluation sprint
For dedicated teams: codebase and architecture audit, CI/CD and environment, and a decision on which modules are LLM-driven and how outputs will be evaluated.
Delivery
Daily stand-ups and sprint reviews. Evals run on every change to prompts or models. The team scales up or down as the roadmap moves.
Which AI projects have our engineers shipped?
Our AI engineers have shipped an OpenClaw agent for a research team in Germany, a production RAG support assistant for a B2B SaaS platform and a self-hosted LLM for a regulated insurer. Each case below links to the full story with the numbers, the team size and the timeline. The client figures come from the clients.
Research turnaround from 2 to 3 weeks to 1 day
Helsingor, education research, Germany
An OpenClaw agent across Slack, Telegram, Gmail and Google Calendar. The client reports 90% fewer mistakes, for an investment under $10,000.
Read the case45% lower first-response time
RAG support assistant, B2B SaaS
A broken RAG prototype taken to production in 6 weeks by 3 engineers, with 31% fewer Tier-2 escalations.
Read the case81% lower three-year cost than an external API
Self-hosted LLM, insurance group, DACH
Six weeks to production, with DPIA, DORA assessment, penetration test and legal sign-off done before go-live.
Read the case
Staff augmentation or a dedicated AI team?
Choose staff augmentation when you already run a product team and need to add LLM, RAG or evals capacity. Choose a dedicated team when you want a unit that owns an AI product or feature end to end, with QA and DevOps included. Both models draw on the same vetted senior engineers.
Frequently asked questions
Share the use case, the data it touches, your stack and your timeline. Valletta replies with whether it needs one engineer or a team and sends a shortlist of vetted senior AI engineers. You interview them, choose staff augmentation or a dedicated team, and most engagements are running inside two weeks.
Generative AI developers build LLM applications, retrieval augmented generation systems and agents that complete multi-step work. They handle prompt design, fine-tuning, evals and safety, plus integration with OpenAI, Azure AI or a self-hosted model. Typical results are knowledge assistants, support automation, report generation and agents that work inside your existing tools.
A working AI proof of concept in days and an MVP in weeks. Our engineers first test the model, the retrieval or the agent on your real data and set up evals, so you learn early whether the idea holds. If it does, they harden it into an MVP your customers can use, with evals on every change.
Yes. With staff augmentation, vetted AI engineers join your team, your stand-ups and your repository for as long as you need them, on a monthly rate per engineer. There is no long-term contract. If someone is not the right fit, we replace them from the same bench at no cost.
No. We do not train foundation models or run ML research, and we decline those projects. Our machine learning work covers LoRA fine-tuning, applied computer vision and model benchmarking, alongside generative AI engineering. If your roadmap needs a research team, we tell you on the first call so you can look elsewhere.
Yes. Everything built for you, including prompts, evaluation sets, pipelines and infrastructure code, belongs to you and is documented so your own team can run it. NDA and IP transfer are in place from day one. There are no proprietary dependencies and no long-term contract.
Tell us what the model should do
Describe the use case, the data it will read and where it has to run. We reply with whether it is a one-engineer or a team job and how we would staff it.
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