This is a 100% hands-on individual contributor role where you’ll build the AI engines behind our platform—automated image processing, generative content creation, intelligent workflows, and large-scale ML pipelines. You’ll work across computer vision, generative models, automation, and ML infrastructure to deliver production-ready AI systems. • Computer Vision & Image Understanding ● Product image analysis, object detection, segmentation ● Automated background removal, image enhancement, preprocessing ● Classification, attribute extraction, and visual search systems ● Quality assessment and edge-case detection models ● Depth estimation and scene understanding from 2D images ● Real-time object detection for AR try-on ● Multi-view image analysis and camera pose estimation • Generative AI & Content Creation ● Fine-tune generative models for visual and marketing asset creation ● Text-to-image and image-to-image model pipelines ● AI-generated product descriptions, tags, and metadata ● Work with diffusion models, GANs, transformers ● Texture generation, style transfer, image editing tools ● Synthetic data generation pipelines ● Experiment with the latest foundation models and diffusion techniques • Intelligent Automation & ML Systems ● End-to-end automation for large-scale product catalog processing ● Recommendation and personalization models ● Automated workflows for QC, moderation, and validation ● Predictive models for engagement and conversion ● Anomaly detection and platform monitoring ● Continuous learning and self-improving systems • Production ML Infrastructure ● Deploy/optimize ML models on AWS ● Build scalable inference pipelines (low latency, high throughput) ● Implement model versioning, A/B testing, CI/CD for ML ● Data pipelines for annotation, augmentation, and quality control ● Optimize models for speed, efficiency, and cost ● Monitoring systems for drift, quality, and performance ● APIs/microservices for ML model serving • Research & Innovation ● Explore latest AI/ML trends and cutting-edge models ● Prototype quickly with state-of-the-art models (GPT-4V, Diffusion, SAM, etc.) ● Integrate open-source tools into our production stack ● Run feasibility experiments and contribute to model architecture decisions ● Document learnings and share insights internally • Technical Stack: - AI/ML Frameworks ● PyTorch, TensorFlow, Hugging Face ● OpenCV, YOLO, Detectron2 ● Stable Diffusion, ControlNet, Diffusers ● scikit-learn, XGBoost • Deployment & Infrastructure ● FastAPI, ONNX, TorchScript, TensorRT ● AWS (SageMaker, Lambda, EC2, S3) ● Docker, Kubernetes ● PostgreSQL, Redis, MongoDB, Pinecone • Languages & APIs ● Python (primary), JavaScript/Node.js (working knowledge) ● REST, GraphQL, WebSocket • Nice to Have (3D/Graphics) ● Understanding of rendering pipelines ● Familiarity with glTF/USDZ ● Experience with Three.js or Unity/Unreal • Must-Haves ● 5-8+ years experience in AI/ML, strong computer vision background ● Deep expertise in PyTorch/TensorFlow ● Production ML deployment experience ● Strong understanding of CNNs, transformers, detection, segmentation ● Hands-on experience with diffusion models or GANs ● Strong Python skills and ML system design ● Cloud experience (AWS/GCP/Azure) ● Proven record of shipping ML products ● Passion for experimenting with new AI models • Highly Desirable ● Experience in e-commerce/retail imaging or content pipelines ● Background in automation and intelligent workflow systems ● Recommendation/personalization experience ● Familiarity with multimodal models (vision + language) ● Experience with neural rendering or 3D generation ● Open-source contributions or research publications ● Real-time inference optimization ● Strong MLOps understanding ● Ability to build end-to-end ML-driven features Problems You’ll Solve: ● Automating large-scale product image processing ● Generating high-quality product visuals at scale ● Extracting structured attributes from image datasets ● Reducing manual processes with intelligent automation ● Optimizing inference speed and cost ● Personalizing user experiences with ML ● Monitoring and evaluating models reliably in production