Lead Computer vision engineer

This role is for one of Weekday's clients
Min Experience: 5 years
Location: Gurugram
JobType: full-time

Requirements

About the Role

We are seeking a highly skilled Lead Computer Vision & Gen AI Engineer with 5 years of experience in designing and implementing diffusion models for furniture replacement in images and achieving ultra-realistic re-rendering. If you thrive on solving challenging AI problems and are passionate about creating innovative solutions in the field of computer vision and image generation, join us to redefine the future of virtual furniture visualization!

Key Responsibilities

• Design, develop, and optimize by fine-tuning diffusion models tailored for furniture detection, replacement, placement in images, and ultra-realistic rendering.

• Create ultra-realistic image re-rendering pipelines for seamless integration of furniture in diverse environments.

• Collaborate with product and design teams to understand requirements and deliver impactful AI-powered solutions.

• Research and implement state-of-the-art generative AI techniques for photorealistic image synthesis.

• Train, fine-tune, and deploy models, ensuring high accuracy and performance on large-scale datasets.

• Develop scalable APIs and systems to integrate model capabilities into production workflows by working closely with the DevOps Engineer.

Requirements

Experience:

• 3-4 years of experience in Generative AI with a focus on diffusion models.

• Demonstrable experience in furniture or object replacement and re-rendering using generative models.

Skills:

• Strong proficiency in frameworks like PyTorch.

• Expertise in fine-tuning diffusion models (e.g., Stable Diffusion, Denoising Diffusion Probabilistic Models).

• Expertise in distributed training using CUDA.

• Solid knowledge of GANs, NeRF, or other generative models for realistic image synthesis.

• Familiarity with 3D rendering pipelines, textures, and lighting models.

• Proficiency in creating and working with large datasets and optimizing model performance.

Soft Skills:

• Strong analytical and problem-solving skills.

• Excellent communication and ability to explain complex ideas and model architectures clearly.

• A proactive and collaborative mindset for teamwork.