Hire Expert AI Developers — LLM, RAG, Agents & Generative AI
Hire pre-vetted AI developers specialising in LLM integration (GPT-4o, Claude 3.5, Gemini), RAG pipeline design, AI agents (LangChain, LangGraph), vector databases (Pinecone, pgvector), fine-tuning, and production AI product development. Dedicated, part-time, or hourly. Start in 3–5 business days.
.svg.png){kind=logo}
AI Development Skills & Expertise
LLM integration, RAG systems, AI agents, prompt engineering, fine-tuning, conversational AI, semantic search, AI microservices, multimodal AI, and AI architecture advisory.
LLM Integration & AI Feature Development
Production LLM integration into web and mobile applications — OpenAI GPT-4o/Claude/Gemini API integration, streaming responses with Server-Sent Events, multi-turn conversation management, system prompt design and optimisation, function calling and tool use, structured output with Zod validation, and AI feature development in Next.js, FastAPI, Node.js, or Django backends.
RAG Pipeline Design & Implementation
End-to-end RAG system development — document ingestion and chunking strategy (fixed-size, recursive, semantic, document-aware), embedding generation with OpenAI text-embedding-3 or open-source models, vector database integration (Pinecone, Weaviate, Qdrant, pgvector/PostgreSQL), hybrid search (dense vector + BM25 keyword), re-ranking with cross-encoders, context assembly, and RAG evaluation (RAGAS metrics: faithfulness, answer relevancy, context recall).
AI Agents & Agentic Workflows
AI agent design and implementation — tool-calling agents (web search, code execution, database queries, API calls), multi-step reasoning with chain-of-thought, LangChain and LlamaIndex agent frameworks, OpenAI Assistants API with persistent threads, ReAct (Reason + Act) agent patterns, agent memory (short-term conversation, long-term vector-stored), multi-agent orchestration (AutoGen, CrewAI), and agent evaluation and safety guardrails.
Prompt Engineering & Optimisation
Systematic prompt engineering — few-shot and zero-shot prompt design, chain-of-thought and tree-of-thought prompting for complex reasoning, role and persona design, output format constraints, prompt versioning and A/B testing, context window management for long documents, and prompt injection defence for user-facing AI applications. We treat prompts as code — version controlled, tested, and optimised.
Fine-Tuning & Model Customisation
Model fine-tuning for domain-specific tasks — OpenAI fine-tuning API (GPT-4o-mini, GPT-3.5-turbo) for consistent output formatting and specialised knowledge; LoRA/QLoRA fine-tuning of open-source models (Llama 3 8B/70B, Mistral 7B, Phi-3, Qwen 2.5) using HuggingFace PEFT library; supervised fine-tuning dataset preparation from production conversation logs; and DPO (Direct Preference Optimisation) for alignment.
AI Chatbot & Conversational AI Development
Production conversational AI products — multi-turn AI chatbots with conversation history management, streaming UI with Vercel AI SDK or direct SSE, intent classification and slot filling, fallback handling and graceful degradation, chat widget integration into web apps, voice interface integration (Whisper STT, TTS), and conversation analytics and monitoring for quality improvement.
AI Tools & Technologies
OpenAI GPT-4o, Claude 3.5, Gemini, AWS Bedrock, LangChain, LangGraph, LlamaIndex, Pinecone, pgvector, Weaviate, HuggingFace, vLLM, FastAPI, Vercel AI SDK, RAGAS, LangSmith, and the full AI engineering stack.
How to Hire an AI Developer
Full-time dedicated AI developer, part-time AI engagement, or hourly/project-based AI sprint — choose the model that fits your AI product stage.
- SaaS products adding AI features (chat, search, summarisation)
- AI-first startups building their core AI product
- Teams that need LLM expertise they do not have internally
- Enterprises building internal AI tools on proprietary data (RAG)
Available within 3–5 business days
- Products with existing AI features that need iteration and improvement
- Teams adding AI to a specific part of their product
- AI feature maintenance and prompt optimisation
- Evaluating AI approaches before committing to full-time hiring
Available within 2–3 business days
- Building a specific RAG system or chatbot
- LLM API integration into an existing product
- AI architecture review and recommendations
- Fine-tuning or prompt optimisation for a specific task
Can start within 24 business hours
Our AI Developer Hiring Process
From AI requirements to first commit in 3–5 business days — AI-specific vetting, your interview, optional proof-of-concept, and ongoing AI quality reviews.
Tell us about your AI project — the LLM providers you want to use (or whether you are open to recommendations), the type of AI feature you are building (RAG, chatbot, agents, search, generation), your existing tech stack (Python, Node.js, Next.js), data privacy requirements (cloud APIs vs self-hosted models), and your team's existing AI knowledge. The context helps us match an AI developer with the right LLM provider experience and architecture depth.
Within 24 business hours, we send you 2–3 pre-vetted AI developer profiles — each with their specific LLM integration experience (which APIs, RAG architectures built, agent frameworks used, production AI products shipped), Python or TypeScript stack preferences, and context about their approach to cost management, evaluation, and AI safety.
Interview the shortlisted AI developers on your specific use case. Ask them to design a RAG architecture for your data, describe how they would manage context window limits, explain their approach to hallucination reduction, or walk through a fine-tuning vs RAG decision. We want you to see real AI engineering reasoning, not rehearsed answers.
For AI projects, we recommend an optional paid 1–2 week proof-of-concept before a longer engagement — building a minimal working version of your key AI feature (a basic RAG pipeline, a working LLM integration, or an agent prototype). This validates the technical approach, lets you evaluate the developer's AI problem-solving depth, and de-risks the full engagement.
Once selected, the AI developer joins your communication channels and repositories. They set up local AI development environment (API keys, vector database access, model testing infrastructure), review your existing codebase and data, and attend sprint planning. Our account manager handles onboarding formalities — NDA, IP assignment, and working hours agreement.
AI products require continuous iteration — prompt improvement, retrieval quality tuning, model upgrades as better versions release, and cost optimisation as usage scales. Monthly check-ins assess delivery quality, AI feature performance metrics, and engagement satisfaction. If the developer is not the right fit, rapid 5-day replacement at no extra cost.
What Our Clients Say
CTOs, Engineering Leads, and Product VPs across the US, UK, and Australia on hiring AI developers from 1Solutions.
We needed to add AI chat to our SaaS product — customers asking questions about their own data. 1Solutions matched us with an AI developer who designed our RAG architecture on pgvector, built the ingestion pipeline, integrated GPT-4o with streaming, and shipped the feature in 6 weeks. Our CSAT for the AI feature is 4.8/5. He joined as full-time after the project.
We had internal AI tools built by a previous contractor that were hallucinating 30% of the time and costing $8K/month in API fees. 1Solutions sent us an AI developer who audited the prompts, redesigned the RAG chunking strategy, added a re-ranking step, and implemented GPT-4o-mini for cheaper tasks. Hallucination rate dropped to under 4%, costs down to $1.2K/month.
We hired an AI developer from 1Solutions to build our AI agent framework — a multi-step agent that researches companies, drafts outreach messages, and logs results to our CRM. She built it in LangGraph with GPT-4o tool calling, handled the rate limiting and retry logic, and built a monitoring dashboard for agent runs. Saves our sales team 3 hours per day.
Why Hire AI Developers From 1Solutions
Production AI experience, LLM-provider agnostic, RAG architecture depth, honest fine-tuning advice, cost and latency engineering, AI safety guardrails, Python and TypeScript coverage, and automated evaluation pipelines.
Production AI, Not Just Demos
Building a GPT-4o chatbot that works in a demo is easy. Building one that handles edge cases, manages context gracefully, doesn't hallucinate on out-of-scope questions, stays within API cost budgets, and works reliably under load — that is what our AI developers deliver. We have built production AI products, not just proof-of-concepts.
LLM Provider Agnostic
Our AI developers have experience across OpenAI, Anthropic, Google Gemini, AWS Bedrock, and open-source models (Llama 3, Mistral). They select the right model for each use case — not just the most popular one. They advise on provider trade-offs (cost, capability, latency, compliance, self-hosting options) before writing a line of code.
RAG Architecture Depth
RAG is not just "embed documents and search" — chunking strategy, embedding model selection, hybrid search, re-ranking, context assembly, and evaluation all significantly affect answer quality. Our AI developers design RAG systems that achieve high faithfulness and answer relevancy scores in production, not just in a 10-document demo.
Honest RAG vs Fine-Tuning Advice
Fine-tuning is often not the right answer when RAG would work better — and it is more expensive and complex to maintain. Our AI developers give you honest architectural advice about which approach fits your use case, rather than recommending fine-tuning because it sounds impressive.
Cost and Latency Engineering
LLM API costs and latency are engineering concerns, not afterthoughts. Our AI developers design for cost-efficiency from the start — model tiering (GPT-4o for complex tasks, Haiku for simple ones), prompt caching, response caching, token budget management, and streaming to improve perceived latency.
AI Safety & Guardrails
Production AI products need guardrails — prompt injection defence, output filtering, hallucination detection, PII redaction before LLM processing, and refusal handling. Our AI developers implement safety layers appropriate to your use case and user base, including compliance considerations for regulated industries.
Python + TypeScript Stack Coverage
Our AI developers work in Python (FastAPI AI microservices, HuggingFace, LangChain) and TypeScript/JavaScript (Vercel AI SDK, LangChain.js, Next.js AI features). They can build AI capabilities into your existing stack rather than requiring a separate Python service for everything.
Evaluation Pipelines, Not Just Vibes
AI quality cannot be assessed by eye-balling 10 test outputs. Our AI developers build automated evaluation pipelines — using RAGAS for RAG systems, LLM-as-judge evaluation for generation quality, regression test suites for prompt changes, and dashboards tracking AI performance metrics over time. You know if AI quality is improving or degrading.
Hire an AI Developer Today
Share your AI project requirements — LLM provider preferences, type of AI feature (RAG, agents, chatbot, search), existing tech stack, data privacy constraints, and start date — and we will shortlist pre-vetted AI developers within 24 business hours.
Shortlisted AI developers within 24 business hours
AI-specific vetting — LLM APIs, RAG design, agent frameworks, evaluation
Full-time, part-time, or hourly/project sprint — flexible from day one
Optional 1–2 week paid AI proof-of-concept before longer engagement
5-day rapid replacement guarantee — no penalty
Tell Us Your AI Requirements
Hiring AI Developers — FAQ
Common questions about hiring AI developers — LLMs, RAG, agents, fine-tuning, APIs, costs, and production deployment.
Related Hire Developer Pages
We also provide dedicated ML engineers, data scientists, blockchain developers, and full-stack engineering teams.