Generative AI Engineer

Company Description

Rendair AI is an innovative AI rendering platform designed for architects and designers, providing tools to create high-quality images, edit renders, upscale outputs, and generate videos through an intuitive and user-friendly interface. The platform enables professionals to transform ideas into final visuals within minutes, eliminating the need for complex setups or external tools. Trusted by thousands of architects, design studios, and creators worldwide, Rendair AI delivers fast and reliable results daily. We are dedicated to accelerating creative workflows and enhancing user productivity.

e are looking for a hands-on Generative AI Engineer to help us improve and expand the AI model workflows behind our product. This role is focused on practical implementation: testing models, fine-tuning image workflows, connecting APIs and endpoints, improving image quality, and helping turn new AI capabilities into real product features.

This is not a traditional research-only ML role. We are looking for someone who is technical, curious, fast-moving, and comfortable working directly with generative image models, APIs, and AI tools.

The Role

You will work on the generative AI systems powering Rendair’s product experience. This includes image generation, image-to-image transformation, masking, inpainting, upscaling, prompt workflows, model testing, fine-tuning, and AI-assisted product features.

You will work closely with our AI/product/engineering team to identify the best models and workflows, test them quickly, connect them through APIs or endpoints, and help prepare them for production use.

You do not need to be an architect or have experience with architecture software. What matters most is your ability to work hands-on with AI models, Python, PyTorch, APIs, and generative image workflows.

Responsibilities

Generative Image Workflows

Build, test, and improve generative image workflows using open-source and commercial AI tools.

Work with models and tools such as Stable Diffusion, Flux, ControlNet, LoRA, ComfyUI, RunPod, Replicate, Hugging Face, OpenAI, Claude, Gemini/Nano Banana, and similar platforms.

Improve workflows for text-to-image, image-to-image, inpainting, masking, upscaling, enhancement, prompt-based editing, style control, and output consistency.

Model Testing and Fine-Tuning

• Evaluate new open-source and commercial image models to understand whether they can improve Rendair’s product.
• Train, fine-tune, or customize image models where useful, including LoRAs or other lightweight adaptation methods.
• Prepare datasets, test training configurations, compare checkpoints, and document quality differences.
• Measure and compare model outputs based on image quality, consistency, speed, cost, and reliability.

API and Endpoint Integration

• Connect AI models, tools, and third-party providers through APIs and endpoints.
• Build prototypes that combine multiple AI tools into usable workflows.
• Work with engineers to productionize successful AI workflows.
• Read API documentation, test endpoints, debug integrations, and clearly document how workflows should operate.

AI Agents and Automation

• Use LLMs and AI agents to improve prompt generation, user guidance, image review, quality control, and workflow automation.
• Experiment with OpenAI, Claude, Codex, Cursor, and other AI coding or agent tools to accelerate development.
• Help design AI-assisted experiences that make the product easier, faster, and more powerful for users.

Quality, Speed, and Cost Improvement

• Improve image output quality and generation consistency.
• Help reduce generation time and cost where possible.
• Compare different model providers, workflows, and inference options.
• Balance quality, latency, and cost based on product needs.

Requirements

• Strong programming experience with Python and proficiency in machine learning frameworks such as TensorFlow or PyTorch
• Experience with Generative Adversarial Networks (GANs), transformers, or related generative models
• Ability to conduct research and implement AI advancements in a production environment
• Hands-on experience with generative image models such as Stable Diffusion, Flux, ControlNet, LoRA, ComfyUI, or similar tools.
• Experience training, fine-tuning, or adapting image models for practical use cases.
• Experience connecting APIs, endpoints, AI model providers, or automation workflows.
• Understanding of image generation workflows, including text-to-image, image-to-image, inpainting, masking, upscaling, conditioning, and prompt-based editing.
• Experience using platforms such as RunPod, Replicate, Hugging Face, OpenAI, Claude, Gemini, or similar AI tools.