LLM application development
Full features on top of Claude, GPT or Gemini — streaming UIs, conversation state, token budgeting and graceful fallbacks.
Hire · AI & LLM Engineering
Hire AI developers who ship LLM features with evals, not vibes.
Dedicated LLM engineers for RAG pipelines, chatbots, agentic workflows and fine-tuning — building with Claude, GPT and Gemini behind guardrails, measurement and a provider-abstraction layer.
Shortlist: 48 hours. Onboarding: 72 hours. Contracts: month-to-month.
What they do
What our AI & LLM engineers build.
Production AI features inside real products — grounded, measured and cost-controlled. Not demo notebooks.
RAG pipelines over your data
Chunking, embeddings, hybrid retrieval and reranking over docs, tickets and databases — answers with citations, not guesses.
Chatbots & support assistants
Customer-facing assistants grounded in your knowledge base, with human handoff, conversation memory and tone control.
Agentic workflows & tool use
Multi-step agents that call your APIs with function calling and MCP — bounded by permissions, retries and audit logs.
Model integrations
Claude, GPT and Gemini behind one abstraction — provider failover, model routing by task and cost, and A/B switching.
Vector database architecture
pgvector, Qdrant or Pinecone chosen for your scale — index strategy, metadata filtering and re-embedding pipelines.
Fine-tuning & prompt systems
Versioned prompt libraries, few-shot design and LoRA fine-tunes on open-weight models when prompting hits its ceiling.
Evals & guardrails
Golden-set evals scoring accuracy on your real questions, output schemas, injection defence and refusal paths — before launch, not after.
Document extraction
Invoices, prescriptions, contracts and forms parsed to structured JSON with confidence scores and human-review queues.
Voice & multimodal
Speech-to-text with Whisper, TTS voice output and image understanding — voice bots and camera-based flows included.
AI inside existing products
Retro-fitting AI into a live SaaS, ERP or app — feature flags, per-tenant limits and billing hooks from day one.
Cost & latency engineering
Prompt caching, response streaming, small-model routing and batch pipelines — AI features your unit economics can survive.
Tooling
The AI stack they work in daily.
Model APIs, retrieval infrastructure and the measurement tooling that separates production AI from demos.
Claude API
OpenAI GPT
Google Gemini
Open-weight models · Llama · Mistral
Function calling · Tool use
MCP
LangChain · LlamaIndex
pgvector
Qdrant · Pinecone · Weaviate
Embeddings · Reranking
Hybrid search · BM25
LoRA fine-tuning
Hugging Face
Whisper · TTS
promptfoo · Ragas · evals
Structured output · JSON schemas
Python · FastAPI
TypeScript · Node.js
Streaming · SSE
LangSmith · observability
Engagement models
Three ways to hire an AI/LLM engineer.
All models are month-to-month with a free replacement guarantee and senior code review included.
Flexible block
Pre-agreed block of hours. Best for feasibility spikes, RAG prototypes and prompt/eval audits.
- Start from a 40-hour block
- Weekly hour reports
- Unused hours roll over one month
80 hrs/month
Half an engineer, consistently. Best for iterating one AI feature to production quality alongside your team.
- Fixed daily overlap window
- Same engineer every day
- Upgrade to full-time anytime
Full-time dedicated
Most popular
160+ hrs/month
An LLM engineer embedded in your team — owning your AI roadmap from retrieval to evals to unit costs.
- Exclusive to your product
- Senior team-lead code review included
- Free replacement within 7 days
- 30 days notice to scale down
How it works
From brief to first shipped AI feature in four steps.
AI candidates are screened on systems thinking — retrieval quality, eval design and cost control — not on having played with an API once.
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1
Brief
The use case, your data sources, privacy constraints and where the feature lives in your product.
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2
Match
2–3 screened AI engineers within 48 hours, matched to your use case — RAG, agents or fine-tuning.
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3
Interview
You interview. You decide. Ask how they would measure answer quality — evals should be their first word.
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4
Start
Onboarded within 72 hours — API keys scoped, data access agreed, first working prototype inside two weeks.
Why Smart Desizns
We build with LLMs in our own products.
AI-assisted engineering and LLM tooling run through our own product work daily. We know where LLM features genuinely pay off inside real software — because we operate that software.
Structured data domain
PharmOS
A production ERP full of the extraction and search problems LLMs solve — invoices, medicine catalogues and reports.
Conversational platformAstroOne
A live consultation marketplace — session flows, matching and content generation surface real conversational-AI territory.
Operations platformHoodDesk
Helpdesk, notices and visitor logs — the operational text data where assistants and summarisation earn their keep.
FAQ
Hiring AI & LLM engineers — common questions.
What can an LLM engineer actually add to my existing product?
The highest-ROI patterns we ship: a support assistant grounded in your docs and tickets (RAG), natural-language search over your data, document extraction that replaces manual data entry, and report or content generation inside existing workflows. The engineer starts by identifying which of these fits your product, then ships one measurable feature first.
Which models do your AI developers work with?
Claude, OpenAI GPT and Google Gemini as managed APIs, plus open-weight models where data or cost demands it. They design behind a provider-abstraction layer so you can switch or mix models without rewriting the product.
How do you stop the AI from making things up?
Grounding and measurement: RAG with citations back to source passages, constrained output schemas, refusal paths when retrieval confidence is low, and an eval suite that scores accuracy on your real questions before and after every prompt or model change. No launch without a baseline.
Can they work with sensitive or private data?
Yes. Options include zero-retention API agreements, PII redaction before the model call, region pinning, and self-hosted open-weight models with pgvector or Qdrant on your own infrastructure when data cannot leave your environment.
What does a dedicated AI/LLM engineer cost?
Rates depend on seniority and scope — a RAG feature inside an existing product is a very different brief from a fine-tuning programme. Send a short brief and we quote in writing within a day, with no obligation until you have interviewed the candidates. For the backend around the AI, see our Node.js developers.
Have an AI feature in mind?
Describe the use case and your data. You will have 2–3 screened AI/LLM engineer CVs within 48 hours — and an honest read on whether the idea is worth building.
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