Hire AI developers in Moldova
ML, LLM apps, and AI agents / MCP servers — built by a Moldova team that actually ships them. Employ them or take a project team. 7% tax, EU overlap.
Get a quoteLocal Moldova team, done for you
Real AI/ML
Machine learning, computer vision, NLP, LLM apps, RAG, and data pipelines — production, not demos.
Agents & MCP
Custom AI agents and MCP servers wired into your stack — a rare skill most Moldova shops don't have.
Employ or contract
Take a project team, or employ your AI engineers under our Moldova entity (EOR).
7% tax, EU time zone
Lower cost via the 7% IT Park tax, with real working-hour overlap.
What you actually get
The deeper breakdown — engagement models, stack breadth, quality, security, and handover.
LLM apps
Chatbots, doc-QA, workflow copilots — with grounding, structured outputs, and guardrails, not just a prompt and a hope.
AI agents & MCP servers
Custom agents for Claude, Codex, and Cline; MCP servers that wire your CRM, database, and internal tools into agent workflows. Rare skill, and one we ship.
Retrieval-augmented generation
Embeddings, vector stores (pgvector, Pinecone, Weaviate), hybrid retrieval, re-ranking, evaluation — the whole RAG loop, tuned for your corpus.
Evals & safety
Test harnesses that catch regressions before users do: golden sets, offline scoring, red-team prompts, latency and cost budgets. The unglamorous half that makes AI ship.
Classical ML
Regression, classification, ranking, computer vision (detection, segmentation), NLP (extraction, classification), time series — the pre-LLM toolbox is still the right one for many problems.
MLOps & pipelines
Training pipelines, model registries, serving (vLLM, Ollama, TGI, Bedrock, Vertex, OpenAI-compatible APIs), monitoring for drift and cost. Production, not notebooks.
From use case to prod, without the demo trap
Use-case scoping
A one-hour conversation about the user, the data, and what "good" means. Most AI projects die in scoping — we spend the time there so the build phase is boring.
AI-specific vetting
Every engineer clears an LLM-app coding round, a system-design conversation about your specific pattern (agent / RAG / classical), and a live pairing session.
POC first
A 2–4 week fixed-price proof of concept on your real data, with a written eval against a golden set. If the model cannot clear the bar, we tell you before you scale it.
Scale to production
MLOps, evals in CI, latency and cost monitoring, agent workflow, and integrations. The POC becomes a service, not a slide deck.
Pragmatic, production-grade AI — not research
What "AI dev" actually means when the goal is a shipped feature and not a paper.
The Moldovan AI talent pool is smaller than the React or backend one, but it is deep where it matters: engineers who have taken LLM apps to production, teams that run classical ML for real customers, and a small number of specialists who have built AI agents and MCP servers wired into real business systems. HireMD is one of the few Moldovan teams that has shipped Claude Code and Codex integrations, custom MCP servers over internal databases, and evaluation harnesses that catch regressions before deploy — which is why we pitch AI as a separate service rather than one more line on a generic dev landing page.
The single biggest differentiator in AI right now is agents and MCP. Most agencies still bolt an LLM call onto a form and call it "AI-powered". A real agentic system needs tool definitions, a well-scoped MCP server, retrieval that returns the right chunks, evaluation that catches when the model drifts, and guardrails that fail closed on out-of-scope requests. We have built enough of them (for our own directory and for clients) that we can size the work honestly and quote a POC that actually resolves the "does this work" question in weeks, not quarters.
On the classical side — regression, classification, computer vision, NLP — the picture is the same story as elsewhere: pick the smallest model that clears the accuracy bar, deploy it behind a stable API, monitor for drift, retrain on a schedule. What Moldova brings is engineers who have done this pipeline end-to-end for actual clients, at a loaded cost that makes running the POC in the first place feasible. Under the 7% IT Park regime senior AI/ML engineers land around $35–65/hr, which is a fraction of the West and puts the "build a POC first" conversation firmly in reach.
Tell us what to build
Describe your AI use case — we reply within one business day with feasibility and a quote.
Frequently asked questions
Can you build AI agents / MCP servers?
Yes — that's a core differentiator. Custom agents and MCP servers for Claude, Codex, and Cline, wired into your CRM, database, and internal tools. We have shipped MCP servers over Postgres, Supabase, GitHub, and custom internal APIs, plus agentic workflows with tool use, retrieval, and evaluation.
What AI stacks do you use?
Python, PyTorch and TensorFlow, LLM APIs (OpenAI, Anthropic, Google Vertex, Bedrock), the Anthropic MCP SDK, LangChain and LlamaIndex where they help, vector stores (pgvector, Pinecone, Weaviate), evaluation frameworks (custom eval harnesses, promptfoo). If you want an on-prem or open-weights model (Llama, Mistral, Qwen) served via vLLM or Ollama, we do that too.
How much do AI developers cost in Moldova?
Senior AI/ML engineers typically run $35–65/hr — a premium skill at a fraction of Western rates. Specialists (MCP, evals, MLOps) sit at the top of that band. Under the Moldova IT Park 7% regime the loaded cost is dramatically below London, Berlin, or the Bay Area for comparable output.
Can you employ the AI engineer for us?
Yes — under our Moldova entity (Employer of Record) with a full Moldova labor-code contract, IP assignment, and payroll under the 7% regime. Or as a project team on time-and-materials. Both models are common; teams building a permanent AI capability usually pick EOR, teams testing a use case pick a POC-then-decide arrangement.
Do you build only LLM apps or also classical ML?
Both. Classical ML (regression, classification, ranking, computer vision, NLP, time series) is still the right tool for many problems and often cheaper to run than an LLM call at scale. We size the tool to the problem rather than reaching for the newest model out of habit — sometimes an XGBoost pipeline beats a fine-tuned LLM at a tenth of the cost.
What LLM providers do you support?
Anthropic (Claude, MCP), OpenAI (including Structured Outputs and Assistants), Google (Gemini, Vertex AI), AWS Bedrock, plus on-prem / open-weights (Llama, Mistral, Qwen) served via vLLM, Ollama, or TGI. If your compliance or cost picture requires self-hosting, we design for that from day one.
How do you handle training data and privacy?
Standard: signed DPA aligned with GDPR, data-processing agreements before we touch anything sensitive, secrets in a vault, and if the compliance picture requires it, an on-prem or self-hosted model with no data leaving your environment. We do not train on client data without explicit written permission, and even then only with the retention rules the DPA specifies.
What is a realistic cost for an AI POC?
For most LLM-app or agent proofs of concept: a 2–4 week engagement, one senior AI engineer and a part-time reviewer, typically $15–35k depending on scope and evaluation depth. That includes real data ingestion, a working prototype, and a written eval against a golden set — so the decision to scale (or not) is grounded, not vibes-based.
Ship AI with a team that actually builds it
ML, LLM apps, agents & MCP — project team or employed under our entity.