Senior AI/ML Engineer – Computer Vision & Generative AI – 5+ yrs exp – BLR location – Up to 25 LPA.

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.

1. 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
2. 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
3. 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
4. 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
5. 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
6. Technical Stack: – AI/ML Frameworks
   ● PyTorch, TensorFlow, Hugging Face
   ● OpenCV, YOLO, Detectron2
   ● Stable Diffusion, ControlNet, Diffusers
   ● scikit-learn, XGBoost
7. Deployment & Infrastructure
   ● FastAPI, ONNX, TorchScript, TensorRT
   ● AWS (SageMaker, Lambda, EC2, S3)
   ● Docker, Kubernetes
   ● PostgreSQL, Redis, MongoDB, Pinecone
8. Languages & APIs
   ● Python (primary), JavaScript/Node.js (working knowledge)
   ● REST, GraphQL, WebSocket
9. Nice to Have (3D/Graphics)
   ● Understanding of rendering pipelines
   ● Familiarity with glTF/USDZ
   ● Experience with Three.js or Unity/Unreal
10. 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
11. 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