Hire Generative AI
Developers
Text, image, audio, and multimodal AI built into real products — with quality gates, cost control, and output pipelines that hold up beyond the demo. US, UK & EU companies, served from India.
What our generative AI developers build for you
Across modalities and use cases — all of it built with quality evaluation, cost tracking, and the kind of pipeline engineering that works in production, not just in isolation.
Text Generation & LLM Applications
Copilots, document automation, structured output extraction, and content pipelines — built with the right model for your use case, not defaulting to whatever's most expensive.
Image Generation & Fine-Tuning
Stable Diffusion, DALL-E, and FLUX integrated into real products — with ControlNet, LoRA fine-tuning, and DreamBooth for style and subject consistency. Not just API wrappers around a generation endpoint.
Multimodal AI (Vision + Language)
Document understanding, image Q&A, visual data extraction, and screenshot-to-insight pipelines using GPT-4o Vision, Gemini, LLaVA, and document-specific models like Florence.
Speech & Audio AI
Whisper-based transcription pipelines, TTS with voice selection, real-time speech processing, and audio classification — integrated into your product's existing UX.
GenAI Feature Integration
Adding generative features to an existing SaaS or platform without rewiring the whole product. Scoped, backward-compatible, and designed around what your users actually need to generate.
Content Automation at Scale
Batch generation pipelines — product descriptions, personalised outreach, localised content — with quality gates, human-in-the-loop checkpoints, and monitoring for output drift.
Hire generative AI developers the way you work
Staff Augmentation
GenAI expertise added to your team
A generative AI developer embedded in your product team — owning the AI feature layer while your existing engineers own the rest. Useful when you know what to build but not how to build it with GenAI.
Best for
Product teams adding their first generative feature.
Dedicated GenAI Pod
Full GenAI product built end-to-end
GenAI developer plus backend engineer working as a unit — model selection, pipeline architecture, frontend integration, and quality evaluation from first commit to launch.
Best for
Companies building a generative AI product from scratch.
Project-Based
Scoped GenAI build
A defined engagement around a specific use case: an image generation feature, a document automation pipeline, a voice interface. Deliverables and milestones agreed before work starts.
Best for
Well-scoped generative use cases with a clear definition of done.
Technologies our generative AI developers work with
GPT
Claude Sonnet
Gemini
LLaMA
Mistral
DeepSeek
Qwen
Stable Diffusion
FLUX
DALL-E
Whisper
LangChain
Hugging Face
PyTorch
TensorFlow
Replicate
Python
TypeScript
FastAPI
Next.js
SQLWhy global companies hire generative AI developers through Artinoid
Generative AI is the easiest technology to demo and the hardest to productionise. Our developers live in the gap between the two.
Products, Not Demos
Generating output in a notebook is straightforward. Building a generative feature that handles edge cases, stays on-brand, manages cost per generation, and doesn't drift in quality over time — that's the hard part. Our developers have done the hard part.
Faster Than In-House Hiring
Generative AI moves fast, and the developers who keep up with it are hard to find through a standard hiring process. We've already vetted them. You get matched in days, not months.
40–60% Lower Than US/UK Rates
Senior generative AI expertise from India — deep in the model landscape, current on every major release — at a fraction of US or UK market rates.
No Lock-In
Generative AI scope evolves as you discover what's possible. Two weeks' notice, weekly billing — structured for teams whose roadmap changes as the technology does.
Simple process, faster than in-house hiring
Discovery Call
We learn about your goals, team structure, timeline, and what a successful engagement looks like for you.
Candidate Matching
We shortlist pre-vetted candidates whose skills and experience closely match your requirements.
Technical Interview
You interview shortlisted candidates directly — same process as in-house hiring. You decide who joins.
Contracts & NDA
Agreements signed swiftly. IP assignment, confidentiality, and data handling are all covered before work begins.
Onboarding
Your new team member is set up, briefed, and contributing from day one. No extended ramp-up.
Common questions
What's the difference between a generative AI developer and a general AI engineer?
A generative AI developer specialises in models that produce output — text, image, audio, code — rather than models that classify or predict. The specific skills diverge: prompt design, diffusion model fine-tuning, multimodal pipelines, output quality evaluation, and the UX patterns for content that's generated rather than retrieved. There's overlap, but they're different disciplines at depth.
Can you fine-tune image generation models on our brand or products?
Yes. For brand consistency in image generation we use LoRA fine-tuning on Stable Diffusion or FLUX — training on a set of reference images to lock in style, character, or product appearance. For subject-specific consistency (a specific product, face, or object) we use DreamBooth. Both approaches run on your dataset, not a generic one.
How do you handle output quality and brand consistency at scale?
Quality gates built into the pipeline — automated scoring against a reference set, flagging for human review, and rejection + regeneration for outputs that fall below threshold. For text generation we also use structured output schemas and multi-pass validation. For image generation, CLIP-based scoring against a style reference. The pipeline doesn't just generate; it filters.
Can you integrate generative AI into our existing product without a rewrite?
Yes — and 'surgical integration without a rewrite' describes most of our GenAI feature work. We build the generative layer as a standalone service that connects to your existing architecture via API. Your existing codebase stays intact; the AI feature plugs in at the right point in the user flow.
What does content automation at scale actually involve technically?
A batch pipeline: input data in (product catalogue, user profiles, campaign briefs), model calls with structured prompts, output validation against quality criteria, optional human review queue for flagged outputs, and write-back to your CMS or database. Rate limiting, cost tracking per unit of output, and retry logic for failed generations are all part of the build — not afterthoughts.
How do you keep generative AI costs under control?
Model routing (using smaller models for simpler tasks), aggressive caching for deterministic outputs, prompt compression to minimise token usage, and batching where latency allows. For image generation, we pick the right model size for the required output quality — not always the most capable one. We track cost per generation from day one.
Do your developers sign NDAs before project details are shared?
Yes. NDA and IP assignment agreements are signed before any project specifics are discussed — including your prompts, training data, and generated content. Standard for every engagement.
Ready to hire generative AI developers?
Tell us what you're building — the modality (text, image, audio), the use case, and where you're at today. We'll scope the right engagement and match you within 48 hours.
contact@artinoid.com
Response Time
Within 24 hours
Next Step
Discovery call to scope your generative AI requirements