Hire Remote Generative AI Specialists

Hire Generative AI Specialists Who’ve Shipped Production AI Products

Every company is building a generative AI feature. Most will ship a demo. Few will ship a reliable, scalable, production product that users trust. The generative AI specialists you need have already navigated that gap — building image generation pipelines for Fortune 500 media companies, AI content engines for high-growth SaaS platforms, and code generation tools for enterprise developer teams.

We match you with senior GenAI specialists who understand the full stack: fine-tuning diffusion models, building production inference APIs, implementing safety and moderation layers, and measuring the quality of generative outputs systematically — not just eyeballing samples.

Start in days, not months. Pay 50% less than equivalent US-based GenAI talent.

What Our Generative AI Specialists Build

Image & Video Generation Pipelines

Stable Diffusion fine-tuning and LoRA/DreamBooth customization for branded image generation. Sora-style text-to-video pipelines. ControlNet-based layout-consistent generation. Production inference APIs handling thousands of image requests per hour.

AI Content & Copywriting Engines

LLM-powered content generation systems with brand voice consistency, factual grounding, tone controls, and human-in-the-loop review workflows. Built for high-volume content operations, not one-off demos.

Code Generation & Developer Tools

GitHub Copilot-style code completion systems, AI-powered code review tools, test generation pipelines, and documentation generation systems — built on fine-tuned Code Llama, DeepSeek Coder, or OpenAI Codex models.

Multimodal AI Applications

Vision-language model (VLM) applications using GPT-4V, LLaVA, and Gemini Pro Vision. Image captioning pipelines, visual question answering, and multimodal search systems.

Synthetic Data Generation

Generative pipelines for augmenting rare event datasets, privacy-preserving synthetic tabular data (using GANs and diffusion), and synthetic training data for downstream ML tasks.

Generative AI Technology Stack

LLM APIs: OpenAI, Anthropic, Google Gemini, Cohere, Mistral, Together AI

Image Generation: Stable Diffusion (SDXL, SD3), DALL-E 3, Midjourney API, Flux

Fine-tuning: LoRA, DreamBooth, Textual Inversion, RLHF, DPO, Axolotl, Kohya

Video Generation: Stable Video Diffusion, Sora API, Runway Gen-3, Pika

Inference: Replicate, Modal, Banana, ComfyUI, A1111, vLLM, TGI

Safety & Evaluation: Llama Guard, OpenAI Moderation, custom NSFW classifiers, CLIP-based scoring

Client Success Story: AI Design Platform — 400% Increase in User-Generated Assets

A Series B creative platform serving 200,000 SMB users wanted to embed AI image generation directly into its design workflow. Our GenAI specialists built a Stable Diffusion SDXL inference API with LoRA fine-tuning infrastructure that let users train brand-specific models on 10–20 reference images in under 3 minutes. A ControlNet-based layout consistency layer ensured generated images adhered to existing design grids. User-generated assets increased 400% in the 60 days post-launch. The AI feature drove a 28% increase in paid plan conversions.

Client Success Story: Enterprise Content Engine — $2.3M Savings in Content Production

A global financial services firm was spending $4M annually on external copywriting for product documentation, email campaigns, and regional market reports. Our GenAI team built a brand-voice-locked content engine using a fine-tuned Llama 3 70B model with a retrieval layer over the firm’s style guides, regulatory constraints, and approved terminology databases. A human-in-the-loop review workflow flagged outputs below a confidence threshold. Content production costs dropped by $2.3M annually. Compliance violations in AI-generated content: zero in 18 months of production operation.

Why Companies Choose Our GenAI Specialists

• Full-stack GenAI: Model fine-tuning, inference infrastructure, safety layers, and quality evaluation — not just API calls
• Production experience: They’ve shipped to real users and handled the reliability, latency, and moderation challenges that come with it
• Quality measurement: They build systematic evaluation pipelines — not just subjective “it looks good”
• 50% cost savings: Senior GenAI expertise at a fraction of US market rates
• Fast start: Most engagements begin within 1–2 weeks

Engagement Models

• Individual GenAI Specialist — One senior generative AI engineer embedded in your team. Ideal for adding image generation, content engine, or multimodal capability.
• GenAI Application Pods (2–4 engineers) — GenAI specialist paired with backend and MLOps engineers. Common for teams launching new AI-native products.
• Full GenAI Teams (5–15+ engineers) — Complete squads for AI platform builds including fine-tuning infrastructure, inference scaling, and safety systems.
• Contract-to-Hire — Evaluate real output before committing long-term.

How To Vet Generative AI Specialists

Our vetting identifies engineers who understand generative model behavior — not just people who know how to call the Stable Diffusion API.

1. Technical screening — Diffusion model architecture (denoising, noise schedules, guidance), LLM sampling (temperature, top-p, top-k), fine-tuning methods (LoRA, full fine-tuning, RLHF), and production challenges (latency, cost per inference, safety). Over 90% of applicants do not pass this stage.
2. System design challenge — Design a production image generation pipeline for a specific use case. Evaluated on quality/cost/latency trade-offs, safety architecture, and monitoring strategy.
3. Live evaluation session — Given sample generative outputs, assess quality, identify failure modes, and propose improvements. Evaluated on systematic thinking, not subjective opinion.
4. Communication screening — Explaining generative AI capabilities and limitations to product teams and executives. We assess this explicitly.

What to Look for When Hiring Generative AI Specialists

Strong GenAI specialists measure quality systematically — they don’t just eyeball samples.

What strong candidates demonstrate:

• They discuss quality evaluation frameworks: FID scores, CLIP scores, human preference benchmarks, and domain-specific metrics
• They’ve implemented safety and moderation layers — NSFW classifiers, prompt injection detection, output filtering
• They understand inference economics: cost per image/token, batch size optimization, caching strategies
• They’ve fine-tuned generative models — they know the difference between LoRA and full fine-tuning and when each is appropriate

Red flags to watch for:

• Equates “generative AI engineering” with calling the OpenAI API — no understanding of model internals or fine-tuning
• No systematic quality evaluation approach — relies on “it looks good to me”
• No experience with safety, moderation, or content policy enforcement
• Has never deployed a generative model to production or managed inference infrastructure

Interview questions that reveal real depth:

• “How do you evaluate the quality of generated images systematically? Walk me through the metrics and human evaluation pipeline you’d set up.”
• “A user is generating images that bypass your content filters 5% of the time. Walk me through your approach to closing that gap.”
• “When would you fine-tune a generative model versus prompting a foundation model? What data and infrastructure requirements change your decision?”

Related Services

• AI Engineers & ML Engineers — Broader AI/ML engineering for teams that need capabilities beyond generative AI.
• Prompt Engineers & LLM Specialists — Specialists in LLM application architecture and RAG systems.
• MLOps Engineers — Infrastructure engineers who scale and monitor your generative AI systems in production.
• Computer Vision Engineers — For teams that need visual understanding alongside visual generation.

Want to Hire Remote Generative AI Specialists?

We source, vet, and place senior generative AI specialists who’ve shipped production AI products — engineers who understand model fine-tuning, inference infrastructure, safety systems, and quality evaluation. Whether you need one GenAI specialist or a complete AI product team, we make it fast, affordable, and low-risk.

Get matched with generative AI specialists →

Ready to hire generative AI specialists who’ve shipped real products? Contact us today and we’ll introduce you to senior GenAI engineers within 48 hours.