Generative AI Engineer / GenAI Developer

You will join an interdisciplinary team building an innovative next-generation digital platform focused on generative graphics pipelines based on hosted diffusion models and multimodal LLMs.

• Production experience shipping generative AI solutions integrating hosted models (Replicate, OpenAI, Google AI, or similar).
• Knowledge of the Flux family and/or SDXL, ControlNet, inpainting, IP-Adapter.
• Experience with ComfyUI or comparable node-graph tools: designing graphs, debugging pipelines, integrating custom nodes.
• Working with LLM APIs: orchestrating Gemini and/or GPT, structured outputs, tool calling, prompt templating, cost and latency control.
• Applied Computer Vision skills: masks, segmentation, layer composition, image preprocessing (resize, format, channels).
• Proficiency in Python at ML engineer level: PyTorch, Diffusers, Transformers, NumPy, OpenCV ecosystem; writing training and evaluation scripts and image processing tools.
• Training data preparation: curation and cleaning of image datasets, automated tagging/captioning (BLIP, CLIP), quality control, class balancing, augmentations, dataset versioning.
• Fine-tuning of generative models with hands-on experience in LoRA, DreamBooth, PEFT for Flux or SDXL.

Nice to have:

• Backend development in TypeScript / Deno (Supabase Edge Functions) or Node.js.
• Experience with PostgreSQL + Supabase (Row Level Security, Storage, Realtime).
• Building custom nodes for ComfyUI.
• Self-hosting generative models on GPU (Replicate Cog, Modal).
• Experience with Kubernetes and Docker for deploying GPU workloads.
• React + TypeScript from the API-consumer side.

• Designing, optimizing and scaling multi-stage workflows in node-graph GUI environments (ComfyUI) and custom backend pipelines.
• Controlling diffusion processes to reduce generation time while preserving visualization quality.
• Working with the Flux family (1.1 Pro, Schnell, Fill Pro, Dev Inpainting + IP-Adapter), SDXL and Gemini Flash Image.
• Developing automatic mask generation pipeline with model escalation, geometry validation and safe fallback for low-quality results.
• Orchestrating Gemini and GPT models for task planning, intent classification, prompt decomposition and input/output sanitization.
• Building comparison infrastructure for model variants and parameters, collecting user feedback, automated result analysis and iterative tuning of default configurations.