Hire Generative AI Engineersin 2 weeks
BearPlex generative AI engineers build production systems that generate (text, images, video, code, audio, structured data) using frontier models, fine-tuned open-source models, and hybrid pipelines. Generation is a different engineering discipline from classification or retrieval; we hire for it specifically.
What a Generative AI Engineer actually does at BearPlex
A generative AI engineer at BearPlex specializes in production systems whose primary job is generating content. The role spans text generation (chatbots, content tools, code generation), image generation (DALL-E, Stable Diffusion, FLUX, Midjourney API), video generation (Runway, Sora, Veo, Kling), audio generation (ElevenLabs, OpenAI TTS, music generation), and structured generation (JSON, XML, code, database schemas). They work with frontier APIs (GPT-5, Claude 4 Opus, Gemini 2.5, DALL-E 3, Midjourney, Runway) and self-hosted open-source generative models (Stable Diffusion XL, FLUX.1, Llama 3.3 for text, Mixtral, AudioGen). They've shipped: marketing content generation tools that produce on-brand copy at scale, code generation systems that build whole features from natural language specs, image generation pipelines for ecommerce product photography, video generation workflows for short-form content marketing, and synthetic data generation pipelines for ML training. They know the production realities of generative work: prompt engineering at the level of measured eval rather than vibes, brand and quality controls, content safety filtering, IP and copyright considerations, and the cost economics of generation (which is dramatically more expensive per call than classification or retrieval).
Sample engineer profiles
Anonymized to respect engineer privacy. Full bios shared under NDA during scoping.
Built a marketing content generation platform for a Series C SaaS: produces 200+ pieces of brand-compliant content per month, replaces 4 FTE writer headcount cost.
Designed an ecommerce product image generation pipeline: generates studio-quality variations from raw product photos, deployed across 80K SKUs.
Shipped a code generation agent for a developer tools startup: generates production-ready API integrations from natural-language specs, used by 5K+ developers.
Built short-form video generation pipeline for a media client: text-to-video with voiceover, music, and captions; 1000+ videos/week production capacity.
Skills matrix
The capabilities every BearPlex Generative AI Engineer brings on day one.
| Skill | Proficiency | Typical tools |
|---|---|---|
| Text generation with frontier models | Expert | Anthropic Claude · OpenAI GPT-4o / GPT-5 · Gemini 2.5 |
| Image generation (managed APIs) | Expert | DALL-E 3 · Midjourney API · Adobe Firefly |
| Image generation (self-hosted) | Expert | Stable Diffusion XL · FLUX.1 · ComfyUI · Forge |
| Video generation | Advanced | Runway Gen-3 · Sora · Veo · Kling · Pika |
| Audio and voice generation | Advanced | ElevenLabs · OpenAI TTS · Suno · Cartesia |
| Code generation systems | Expert | Claude Sonnet for code · Codex · structured prompting patterns |
| Structured generation (JSON, XML, code) | Expert | Pydantic / instructor · function calling · structured output APIs |
| Brand voice and style consistency | Expert | fine-tuning · few-shot examples · evaluation rubrics |
| Content safety and moderation | Advanced | Azure Content Safety · OpenAI Moderation · custom classifiers |
| Generation cost optimization | Advanced | prompt caching · smaller distilled models for high volume · batch processing |
| IP and copyright safety | Advanced | model selection for commercial use · training data provenance review |
| Evaluation harnesses for generative output | Expert | LLM-as-judge · human eval rubrics · automated metrics where applicable |
How we vet generative AI engineers
Technical screen
60-minute deep-dive on past generative AI work. We probe: model selection rationale, evaluation methodology, brand/quality control approach, and what failed in production. We screen out engineers who treat generation as 'just call the API': production generation is a measurement and engineering discipline.
Live generation exercise
We give the candidate a realistic generation problem (content, image, or code generation with quality constraints) and 90 minutes. They must design the prompts, set up evaluation, and iterate against measured failures. We're looking for: rigorous eval, pragmatic model selection, and cost awareness.
Architecture interview
Whiteboard a generation system for a realistic client scenario: high-volume marketing content with brand voice constraints, multi-modal output (text + image), per-customer customization. We probe for: cost economics, evaluation rigor, content safety, and operational thinking.
Reference checks + paid trial
Two engineering reference checks plus a 21-day paid trial on a real client engagement. We don't take engineers off trial until both Hamad and the client engineer report 'I want this person on the team next sprint.'
What clients say
“Their generative AI engineer built our content pipeline in 6 weeks that does what we expected to need 4 writers. The brand voice consistency was the surprise: measurably better than what we'd been getting from human writers without the same eval rigor.”
“Production image generation is harder than the demos suggest. The BearPlex engineer brought the prompt engineering rigor and post-processing pipeline that took our images from 'AI-looking' to 'good enough to ship.'”
“We needed code generation that worked at production quality, not demo quality. Their engineer built an evaluation harness BEFORE the system, which is why it actually shipped.”
Hiring generative AI engineers: questions answered
Yes: increasingly common. We've shipped production systems with DALL-E 3, Midjourney API, Stable Diffusion XL, FLUX.1, Runway, Sora, Veo. The work spans prompt engineering for these models, post-processing pipelines (ComfyUI, custom Python), brand consistency at scale, and integration with downstream systems (DAM, CMS, ecommerce platforms).
Layered approach: detailed system prompts with brand voice principles and examples, few-shot demonstrations of on-brand vs off-brand outputs, light fine-tuning when budget supports it (typically reserved for high-volume cases), and evaluation rubrics that measure brand voice adherence as part of every release. For visual content, we use brand-specific LoRAs trained on the customer's existing assets to constrain visual style consistently.
We take this seriously and design accordingly. For commercial use we recommend models with clear commercial licensing (DALL-E 3, Adobe Firefly, FLUX.1 commercial license, Stable Diffusion XL commercial). We avoid models with unclear training data provenance for clients with strong IP concerns. For client work involving customer-uploaded inputs, we ensure customer ownership and avoid using customer content for further model training.
Yes: common engagement type. Cost optimization techniques: prompt caching (90% discount on cached prefixes for stable system prompts), distillation to smaller models for high-volume tasks (5-20× cost reduction), batch processing for non-real-time workloads (50% discount on OpenAI batch API), and aggressive caching of common outputs. For million-request-per-month workloads, these optimizations often pay back in weeks.
Yes: required for any user-facing generation. We layer: input moderation (filter prompts for unsafe requests), output moderation (filter generated content for unsafe results), brand-safety filters (reject content that violates client brand guidelines), and topic restriction (keep generation within intended scope). Standard tools: Azure Content Safety, OpenAI Moderation, custom classifiers for client-specific rules.
Primarily Lahore, Pakistan (HQ) with client-facing presence in Austin and Doha. Time zone overlap with US clients is 5-9 hours; we structure engagements with daily 2-3 hour overlap windows for synchronous work, async handoff for the rest.
Yes: common for image generation (LoRA fine-tuning of Stable Diffusion or FLUX on customer style) and increasingly for text generation (DPO fine-tuning of open-source LLMs on brand voice or output format). We pair generative AI engineers with our fine-tuning engineers when significant fine-tuning is part of the engagement scope.
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