Hire freelance AI developers
Move from AI experiments to features your users rely on. Hire engineers who have shipped LLM products with evaluation, cost control and security built in.
- Rahul Mehta — Machine Learning Engineer (LLMs & RAG)
- Arjun Patel — AI Engineer — Chatbots & Document AI
What AI developers build in 2026
Most AI projects on Taskora fall into four groups. Assistants and search answer questions from your own documents, product data or knowledge base using retrieval-augmented generation. Document processing extracts structured data from invoices, contracts, forms and emails. Workflow automation uses models to classify, route, draft and summarise inside existing tools. Agents take actions — calling APIs, updating records, running multi-step tasks — with guardrails and human approval where needed. Classic machine learning, such as forecasting, recommendations and computer vision on your own data, remains a separate specialism usually handled by data scientists and ML engineers. Knowing which group your idea belongs to helps you write a clearer brief and find candidates with directly relevant experience rather than general enthusiasm for AI.
The difference between a demo and production
A convincing AI demo can be built in a day. Production is different: it needs an evaluation set that measures quality on real examples, protection against prompt injection and data leaks, respect for user permissions in retrieval, monitoring, cost tracking, fallbacks when a provider is slow or down, and a way to test changes to prompts or models before they reach users. When you compare candidates, ask how they handled each of these on a past project. Developers who talk about evaluation and failure modes without being prompted are the ones who have shipped.
How to structure an AI project
Start with a fixed-price proof of value of two to four weeks on your real data. Define success as a measurable target, such as the share of test questions answered correctly with a cited source, or the accuracy of extracted fields. The developer builds the smallest pipeline that can reach it and an evaluation set to prove it. Only if the target is met do you fund the production build: integration, interface, monitoring and security review. This structure protects your budget, gives the developer a fair target and produces the evaluation assets you need to maintain quality after launch.
Costs to plan for
Budget for development and for running costs. Development of a proof of value typically costs $2,500–8,000; a production RAG assistant integrated into an existing product $12,000–40,000. Running costs depend on volume and model choice: a support assistant answering a few thousand questions a month may cost tens to a few hundred dollars in model usage, while high-volume document processing can cost much more. Ask candidates for a cost estimate at your expected volume and for the levers they would use to reduce it, such as caching, smaller models for simple steps and batching.
Data, privacy and security
Before sharing data, agree on what the developer can access and where it is processed. Use anonymised or synthetic samples for early work where possible, check the data-processing terms of the model providers involved, and make sure the architecture respects existing permissions — a user should never be able to retrieve documents through the assistant that they could not open directly. Ask for a short security review as part of the final milestone and for documentation of prompts, configuration and the evaluation set, so your team can maintain the system.
Red flags when hiring for AI
Be careful with candidates who guarantee a specific accuracy before seeing your data, show only polished demos without any evaluation results, or propose fine-tuning a custom model as the first step for a problem that retrieval and good prompting could solve. Ask how they would handle a wrong answer shown to a customer; vague responses suggest limited production experience. Be wary of anyone requesting broad access to production databases without a clear reason and safeguards, or who cannot estimate running costs at your volume. Strong AI engineers are sceptical of AI where it is unnecessary, and will happily tell you which parts of your problem are better solved with ordinary code.
How much does a freelance AI developer cost?
Experienced AI engineers on Taskora charge $60–100 per hour at mid level and $100–180 as seniors. Proofs of value typically cost $2,500–8,000.
Which models and tools do AI developers use?
Most work with several commercial and open-weight model providers, vector search in PostgreSQL or dedicated databases, and orchestration in Python or TypeScript. A good developer chooses based on your quality, cost and privacy needs.
Do I need to fine-tune a model?
Rarely as a first step. Retrieval, better prompts and structured outputs solve most business problems. Fine-tuning makes sense for narrow, high-volume tasks with lots of examples.
How do I know the AI feature is good enough?
Agree on an evaluation set of real examples and a target score before building. Measure every change against it.
Can AI developers work with sensitive data?
Yes, with care: minimise access, use anonymised samples where possible, choose providers with suitable data-processing terms and document the data flow.
Who owns the prompts and evaluation data?
You do. Include prompts, configuration and the evaluation set in the deliverables of the final milestone.