Cloudesk · AI & LLM Integration Developer
Hire an AI & LLM Integration Developer on Subscription
Hire an AI & LLM Integration Developer on subscription — GPT-4, Claude, RAG pipelines, and production AI features built for your product.
3 seats = 10% off · 5+ seats = 20% off · 7-Day Risk-Free Trial Guarantee
What a AI & LLM Integration Developer seat is
An AI & LLM Integration Developer seat adds a production-grade AI layer to your existing product. Your developer integrates large language models (OpenAI, Anthropic Claude, Mistral, Llama), builds Retrieval-Augmented Generation (RAG) pipelines over your data, and ships AI-powered features — chat assistants, document summarization, semantic search, and automated workflows. Every integration is cost-monitored, latency-profiled, and evaluated for output quality before shipping.
Who this seat is for
- SaaS products adding an AI assistant, copilot, or smart search feature
- Teams with proprietary data who want their AI to answer questions over their own knowledge base
- Startups building AI-native features without in-house ML expertise
- Founders who want production-ready AI integrations, not demos
Skills on this seat
Stacks you can select on Pricing for this seat (+$0 on the fixed package).
- AI Agents & Tool Use
- Anthropic / Claude API
- LangChain / LlamaIndex
- OpenAI API
- Prompt Engineering
- Python / FastAPI
- RAG Pipelines
- Vector Databases (Pinecone / Weaviate)
Duties on your project
What this seat does under your Project Manager.
- Integrate LLM APIs (OpenAI, Anthropic, Mistral, Cohere) with robust prompt engineering and structured outputs
- Build RAG pipelines: document ingestion, vector embedding, similarity search (Pinecone, Weaviate, pgvector)
- Design AI feature architecture: streaming responses, function calling, tool use, and agentic workflows
- Implement AI evals to measure output quality, hallucination rate, and task completion
- Optimize LLM cost and latency: prompt caching, model routing, batching, and token monitoring
- Build AI-powered features: semantic search, document Q&A, content generation, and summarization
Example deliverables
- Production LLM integration with structured prompt templates and output parsers
- RAG pipeline with document ingestion, vector store, and retrieval chain
- AI feature end-to-end: from UI to backend API to LLM response streaming
- Evaluation suite measuring accuracy, latency, and cost per query
- Cost monitoring dashboard tracking token usage and monthly LLM spend
When to choose a different seat
- Training custom ML models from scratch on your data (use a specialized ML engineer)
- Fine-tuning large foundation models (requires specialized GPU infrastructure)
- Pure backend API development without an AI component (use Backend Developer seat)
How the subscription works
- Configure the AI & LLM Integration Developer seat and select your package on Pricing.
- Share your product roadmap, existing API architecture, and the AI features you want to build.
- Your developer proposes an integration design: which models, retrieval strategy, and feature architecture.
- Features are built iteratively, with eval results shared each cycle so quality is measurable from day one.
- Baseline QC validates each delivery. Your PM tracks LLM cost and latency targets.
Compare models on subscription development team, then configure on Pricing.
Seat packages
Dedicated PM, Baseline QC, and daily async standup included on every seat.
Showing Weekly (20% Intro Offer) seat tiers
3 seats = 10% off · 5+ seats = 20% off · All prices USD
Starter
10 hrs/wk$1,050$840/week20% OFFStarter · 10 hours per week on your one active project — dedicated Project Manager, unlimited role-matched tasks.
- Popular
Growth
20 hrs/wk$2,100$1,680/week20% OFFGrowth · 20 hours per week on your one active project — dedicated Project Manager, unlimited role-matched tasks.
Dedicated
40 hrs/wk$4,200$3,360/week20% OFFDedicated · Full 40 hours per week on your one active project — dedicated Project Manager, unlimited role-matched tasks.
AI & LLM Integration Developer FAQ
Which LLM providers do you work with?
OpenAI (GPT-4o, o1), Anthropic (Claude), Mistral, Cohere, and open-source models via Ollama or Replicate. Model choice is guided by your cost, latency, and capability requirements.
Can you build a chatbot over our internal documents?
Yes — this is a classic RAG use case. Documents are chunked, embedded, stored in a vector database, and retrieved at query time to ground the LLM's answers in your content.
How do you handle hallucinations and output quality?
We build eval suites that test outputs against ground-truth answers, measure factual accuracy, and track hallucination rates. Prompt engineering and retrieval tuning are iterated until quality targets are met.
How much will the LLM API cost us per month?
We instrument token usage from day one and provide a cost projection based on your expected query volume. Prompt caching and model routing are used to minimize ongoing LLM spend.
Opens fixed packages with this role ready to add as a seat — add 3+ seats for team discount.