20+ Generative AI Experts - Available Now

Hire Generative AI Developers

Get dedicated Generative AI developers to build LLM-powered products — from custom chatbots and RAG systems to AI agents and content-generation tools.

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Trusted by 80+ teams
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Why choose us?

A boutique shop, not a marketplace.

Pre-vetted engineers only

Every developer passes a 5-stage vetting process.

Dedicated to your project

Your developer works exclusively on your product.

Transparent billing

Hourly time tracking with daily reports. No hidden fees.

Instant replacement

We replace within 48 hours at no extra cost.

Your timezone, your tools

Developers overlap 4-8 hours with your working day.

Scale up or down anytime

Flexible contracts with no lock-in and no penalties.

The exact skill fit for your build.

Generative AI has moved from research demos to real business products in just a couple of years — customer support copilots, document intelligence tools, code assistants, and AI agents that take multi-step actions on a user's behalf. Building these responsibly requires more than calling an API: it takes careful prompt design, retrieval architecture, evaluation, and guardrails to keep outputs accurate and safe at scale.

At ZonSource, our Generative AI developers have hands-on experience building applications on top of models from OpenAI, Anthropic, and open-source alternatives like Llama, using frameworks such as LangChain and LlamaIndex. They design retrieval-augmented generation (RAG) pipelines with vector databases, fine-tune models for domain-specific tasks, and build agentic workflows that chain reasoning and tool use together. Whether you need a proof-of-concept validated, a production-grade AI feature shipped, or a dedicated engineer to own your GenAI roadmap long-term, our team delivers reliable, well-evaluated systems from day one.

ZS
Generative AI Developer
Senior · 5+ years avg.
LLMsPrompt EngineeringRAGLangChainLlamaIndexVector DatabasesFine-TuningOpenAI APIAnthropic APIAI Agents
Current Stack
OpenAI / GPTOpenAI / GPT
Anthropic / ClaudeAnthropic / Claude
LangChainLangChain
Vector DatabasesVector Databases
PythonPython

Skills to look for when hiring.

Every Generative AI developer on our bench is evaluated against these core competencies.

LLM Fundamentals & Prompt Engineering

Strong understanding of how LLMs work and how to design reliable, structured prompts

Retrieval-Augmented Generation (RAG)

Practical experience building pipelines that ground model outputs in your own data

Vector Databases & Embeddings

Skilled with Pinecone, Weaviate, or similar for semantic search and retrieval

Fine-Tuning & Model Adaptation

Ability to fine-tune or adapt models for domain-specific tasks when RAG isn't enough

Agentic Workflows & Tool Use

Experience building AI agents that chain reasoning steps and call external tools or APIs

Evaluation & Guardrails

Knowledge of building evaluation pipelines and safety guardrails to catch hallucinations and misuse

LangChain / LlamaIndex Frameworks

Hands-on experience with orchestration frameworks for building LLM applications

Python & API Integration

Strong Python skills for integrating LLM APIs into production backends

Meet the bench.

A snapshot of Generative AI developers available now.

DA
Dr. Aarav Malhotra
Senior Generative AI Engineer
6 experience
LLMsRAGLangChainVector Databases
Built enterprise RAG systems handling 500K+ document knowledge bases
SL
Simone Laurent
GenAI Engineer (Agents)
4 experience
AI AgentsPythonOpenAI APITool Use
Specializes in multi-step agentic workflows for customer support automation
KA
Kabir Anand
GenAI Engineer (Fine-Tuning)
5 experience
Fine-TuningHugging FacePyTorchEvaluation
Fine-tunes domain-specific models for regulated industries like legal and healthcare
Get a Shortlist Matched to Your Project

The full Generative AI lifecycle.

Everything your project needs, from first line of code to production deployment and beyond.

Custom AI Chatbot & Copilot Development

Build LLM-powered chatbots and copilots tailored to your product or support workflow

RAG Pipeline Development

Build retrieval-augmented generation systems that ground AI answers in your own data

AI Agent & Workflow Automation

Build agentic systems that reason through multi-step tasks and call external tools

LLM Fine-Tuning & Customization

Adapt open-source or foundation models for domain-specific accuracy

GenAI API Integration

Integrate OpenAI, Anthropic, or open-source LLMs into your existing product

LLM Evaluation & Guardrails

Build evaluation pipelines and safety guardrails to reduce hallucinations and misuse

Generative AI Proof-of-Concept

Validate a GenAI use case quickly before committing to full production development

GenAI Maintenance & Support

Ongoing prompt tuning, model updates, and performance monitoring

Simple, transparent pricing.

Share your requirements and we will put together a plan that gives you the best value.

Share Your Requirements · Get a Custom Quote

What clients say about the bench.

DO
Diana Okonkwo
Head of Support - SupportIQ
★★★★★

ZonSource's GenAI team built a support copilot that resolved a third of our tickets without any human involved.

TL
Tobias Lindberg
CTO - DocuMind
★★★★★

Their RAG pipeline made our internal knowledge base actually searchable in plain English for the first time.

MC
Meera Chandrasekhar
Product Lead - CodeForge
★★★★★

The AI agent they built automates a workflow that used to take our team hours every single day.

Requirement to first commit, in under a week.

1

Share Requirements

Tell us your use case, data sources, and desired outcomes

Day 1
2

Developer Shortlist

We match you with 2-3 pre-vetted Generative AI developers

Day 2
3

Interview & Select

Interview shortlisted developers, assess skills and culture fit

Day 3-4
4

Onboard & Start

Developer joins your standups, tools, and workflows

Day 5

When to Hire a Generative AI Developer vs a Traditional Machine Learning Developer

If your goal is to build on top of existing large language models — chatbots, copilots, content generation, RAG-based search, or AI agents — a dedicated Generative AI developer is the right fit. Their expertise centers on prompt design, retrieval architecture, and orchestrating foundation models rather than training models from scratch.

A traditional machine learning developer makes more sense when you need custom predictive models trained on your own structured data — forecasting, classification, fraud detection, or recommendation systems — where a general-purpose LLM isn't the right tool for the job. Many teams need both, and the two roles increasingly overlap as GenAI systems incorporate classical ML components.

Generative AI developer interview questions.

Use these questions when evaluating Generative AI developers — or let us handle the vetting.

Q1. What is retrieval-augmented generation (RAG), and when would you use it over fine-tuning?

What to look for

Tests fundamentals — should weigh cost, data freshness, and use-case fit

Q2. How do you reduce hallucinations in an LLM-powered application?

What to look for

Should mention grounding with retrieval, prompt constraints, and output validation

Q3. How would you design an evaluation framework for a chatbot's responses?

What to look for

Look for structured metrics beyond "it looks good," like relevance and factual accuracy checks

Q4. What is the difference between a vector database and a traditional database for this use case?

What to look for

Should explain semantic similarity search versus exact-match querying

Q5. How do you handle sensitive or regulated data when using third-party LLM APIs?

What to look for

Tests awareness of data privacy, on-prem/open-source alternatives, and compliance considerations

Q6. Walk me through how you'd design an AI agent that completes a multi-step task

What to look for

Look for reasoning about tool use, error handling, and stopping conditions

Q7. How do you manage the cost of running LLM-powered features at scale?

What to look for

Should mention caching, model selection by task complexity, and prompt/token optimization

Before you hire.

What skills should a Generative AI developer have in 2026?
Must-haves include prompt engineering, RAG pipeline design, and experience with LLM APIs from providers like OpenAI or Anthropic. Strong additions include vector databases, fine-tuning experience, and building agentic workflows.
How long does it take to onboard a Generative AI developer?
Most ZonSource Generative AI developers can be onboarded and start contributing within 48 hours of finalizing requirements.
Should we fine-tune a model or use retrieval-augmented generation (RAG)?
RAG is usually faster to implement, cheaper to maintain, and better when your underlying data changes frequently. Fine-tuning makes more sense when you need consistent style or behavior that retrieval alone can't achieve.
How do you prevent an AI chatbot from giving wrong or made-up answers?
We ground responses in your own verified data using RAG, add output validation checks, and build evaluation pipelines that catch hallucinations before they reach users.
Can you build this using open-source models instead of OpenAI or Anthropic?
Yes, our developers work with open-source models like Llama when data privacy, cost at scale, or self-hosting requirements make that a better fit than a third-party API.
Do you offer a trial period before hiring a Generative AI developer full-time?
Yes, we offer a 2-day risk-free trial so you can evaluate the developer's skills before committing long-term.
Will the Generative AI developer work in our timezone?
Our developers offer 4-6 hours of daily overlap with US timezones and full overlap with European and APAC teams.

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