3D Reconstruction & Generative AI Engineer

We are hiring a 3D Reconstruction & Generative AI Engineer to build an end-to-end system that can generate high-precision 3D jewellery models from images and text prompts.

This role goes beyond classical CV you will design systems that combine:

• 3D reconstruction (multi-view / single-view)
• Generative AI (text-to-3D / image-to-3D)
• RAG pipelines for design retrieval and conditioning

The final output must be manufacturing-grade meshes suitable for gold casting / 3D printing.

• Build pipelines for:
• Multi-view 3D mesh reconstruction
• Single-view 3D with learned priors
• Implement:
• Point cloud generation, fusion, and meshing
• Surface reconstruction and refinement
• Ensure:
• High geometric accuracy (sub-mm level)
• Clean topology (watertight meshes)

• Develop and fine-tune models for:
• Text-to-3D generation
• Image-to-3D generation
• Work with:
• Diffusion models (2D 3D lifting)
• NeRF / Gaussian Splatting-based methods
• Latent 3D representations
• Integrate:
• Style control.
• Conditional generation (user constraints)

• Build RAG pipelines for:
• Retrieving existing designs / CAD assets
• Conditioning generative models with reference designs
• Design:
• Embedding pipelines (image + text embeddings)
• Vector databases for design search
• Enable:
• Generate similar to this design workflows
• Hybrid pipelines:
• Retrieval conditioning generation refinement
• Optimize for:
• Low-latency retrieval
• High semantic relevance

• Optimize models for:
• GPU efficiency (multi-GPU setups)
• Batch inference pipelines
• Deploy:
• APIs for generation (image/text 3D)
• Scalable backend systems

• Implement:
• Mesh cleanup (hole filling, smoothing)
• Topology correction
• Conversion to CAD-friendly formats (STL/STEP)
• Ensure outputs are:
• Print-ready
• Structurally valid for casting

• Strong experience in Python + PyTorch
• Solid understanding of:
• 3D geometry (meshes, point clouds, SDFs)
• Multi-view geometry & camera calibration
• Rendering and differentiable rendering
• Experience with:
• PyTorch3D / Open3D / Trimesh
• 3D reconstruction pipelines (SfM, MVS, NeRF)

• Hands-on experience with:
• Diffusion models (Stable Diffusion, latent diffusion)
• GANs / 3D generative models
• Understanding of:
• Text-to-image / text-to-3D pipelines
• Conditioning and prompt engineering

• Experience building:
• RAG pipelines (retrieval + generation)
• Familiarity with:
• Vector databases (e.g., FAISS, Milvus)
• Embedding models (CLIP, multimodal embeddings)
• Ability to:
• Combine structured (CAD) + unstructured (images/text) data
• Design semantic search systems

• Experience in:
• Jewellery design / CAD workflows
• Blender / MeshLab / Rhino / Fusion 360
• Familiarity with:
• Vision-language models
• Grounding models (e.g., object-aware generation)
• Experience with:
• Multi-GPU training (important for your setup)
• Diffusion + 3D hybrid pipelines

• Build a system capable of:
• Text/Image high-quality 3D jewellery
• Retrieval-assisted generation (RAG-powered design system)
• Deliver:
• Production-ready meshes
• Consistent design quality across variations

• 26+ years in:
• Computer Vision / Generative AI / 3D Graphics
• Strong in both:
• Research understanding
• Production deployment

• Text-to-3D systems
• RAG-based generative pipelines
• High-detail reconstruction (jewellery / mechanical parts)
• CAD-integrated AI systems

• Competitive (based on experience and depth in GenAI + 3D)
• BETWEEN 4-8 LPA, BASED ON PERFORMANCE.