Shimira ITHire AI developers
AI developers who've shipped systems users rely on.
The AI talent market in 2026 has a verification problem. Demand exploded, so every resume grew an "AI" section, but building a chatbot over a weekend and building an AI system a legal team trusts with contract review are different professions. The second one is who you're trying to hire.
That second kind is what we staff. Our AI developers built our contract-analysis platform that automated clause identification for legal teams, production AI, measured by its error rate, used by professionals whose time is expensive. That's the bar every engineer we place has to clear.
What they bring
Developers who've done it in production.
LLM application engineering
Copilots, assistants, and AI features built on frontier models (OpenAI, Anthropic, Gemini) or open-weight models in your infrastructure, with prompt architecture, context management, and output validation done as engineering, not experimentation.
RAG and knowledge systems
AI that answers from your data with citations. If your use case is retrieval-heavy or built on a specific stack, our LangChain developers page covers that specialization in depth.
Document intelligence
Extraction, classification, and analysis of contracts, invoices, forms, and reports, structured outputs your downstream systems can consume, validated before anything touches a database.
AI agents and workflow automation
Multi-step pipelines where the model handles judgment inside deterministic scaffolding, with human checkpoints where mistakes cost money.
Evaluation and reliability engineering
The discipline that separates production AI from demos: test sets, accuracy harnesses, hallucination measurement, cost and latency budgets, guardrails, and monitoring.
The integration layer
AI lives inside real software. Our AI developers are backend engineers first, the APIs, queues, and data pipelines around the model come from the same team (Node.js and TypeScript throughout).
- OpenAI
- Anthropic
- Gemini
- LangChain
- pgvector
- TypeScript
Proof
Not a résumé, a running product.
AI story generation running in a live production pipeline, the model is the easy part; making it reliable is the work.
What good looks like
Vetting an AI developer in 2026
- Asks about your evaluation data before your model preference. Engineers who start with "how will we measure correctness?" have shipped before.
- Can quote real numbers from past systems, accuracy achieved, cost per request, latency budgets, not just architectures.
- Model pragmatism: knows when a small cheap model beats a frontier model, and when fine-tuning beats both. Framework and vendor loyalty are yellow flags.
- Hallucination literacy: speaks in mitigation and measurement (grounding, validation, confidence thresholds), never prevention promises.
- Security awareness: prompt injection, PII handling, data-residency options.
- Software fundamentals: can build the API around the model, not just the notebook inside it.
Pricing
Engagement models & pricing.
| Model | Best for | Pricing (2026) |
|---|---|---|
| AI feasibility assessment | Testing viability before committing budget | $3,000 – $8,000 fixed (1–2 weeks) |
| Dedicated AI developer (full-time) | Ongoing AI product work | $7,000 – $12,000/month (senior) |
| AI pod (AI engineer + backend dev + lead oversight) | Building a complete AI product | $14,000 – $28,000/month |
| Hourly engagement | Architecture reviews, model selection, rescues | $40 – $75/hr (senior) |
We staff AI work senior-only, it's the most seniority-sensitive discipline in software, and a mediocre AI system isn't slightly worse, it's unusable. Comparable US AI engineers bill $150–$250+/hour; specialized AI skills carry a 25–60% premium over general development in every region. Budgeting context, including the 15–30% AI adds to any build and ongoing inference costs, is in our software development cost guide.
Two ways to work with us
Hiring developers vs. commissioning the project
Two ways to work with us, and it's worth choosing deliberately:
Hire AI developers (this page)
When you have technical leadership in-house and need embedded AI engineering capacity on your team.
Commission the build
Via our AI development services when you want an outcome, a working system, scoped and delivered, rather than headcount. Most first AI projects fit better here, usually starting with the feasibility assessment.
Not sure which? That's a normal first-call question, we'll tell you honestly.
Questions
What buyers ask before hiring.
Senior AI engineers from Shimira IT: $40–$75/hour or $7,000–$12,000/month dedicated. A complete AI pod runs $14,000–$28,000/month. US equivalents bill $150–$250+/hour. Add ongoing model API costs, which we estimate before any build.
For LLM applications, RAG, and automation, an AI developer (software engineer with LLM expertise). Data scientists fit when the problem is statistical modeling, forecasting, or analytics on your data. Most 2026 business AI use cases need the former.
Yes, NDAs before discovery if you prefer, enterprise API tiers that don't train on your data, and open-weight models inside your infrastructure when data can't leave. Handling is agreed in writing first.
That's exactly what the 1–2 week feasibility assessment is for: we test against your real data and give you achievable accuracy numbers and running costs before you commit to anything larger.
Python and TypeScript, LangChain/LangGraph where it fits (dedicated page here), pgvector or purpose-built vector stores, and evaluation tooling around all of it. Model choice is benchmarked per use case, never assumed.
Related
Not quite what you need?
Tell us the gap. We'll fill it with the right person.
Describe the role and your stack. We'll put forward vetted developers you interview and choose from, usually within a week.