About the Role

We are seeking a talented Computer Vision Engineer with strong academic credentials and research experience to join our AI/ML team. This role focuses on developing and implementing state-of-the-art computer vision solutions for specialized detection and analysis systems. You will work on challenging problems at the intersection of deep learning, forensic AI, and multi-modal analysis.

Key ResponsibilitiesResearch & Development

• Design and implement novel computer vision architectures for complex detection and classification tasks
• Explore and evaluate cutting-edge research papers and integrate promising techniques into production systems
• Develop custom loss functions, training strategies, and optimization techniques for specialized applications
• Conduct rigorous experiments with comprehensive documentation and ablation studies
• Contribute to technical documentation, research reports, and potential publications

Model Development

• Build end-to-end deep learning pipelines for image, video, and multi-modal analysis
• Design and train custom neural network architectures combining CNNs, transformers, and hybrid approaches
• Implement advanced techniques including attention mechanisms, metric learning, and feature fusion
• Optimize models for production deployment with focus on accuracy-latency trade-offs
• Develop robust evaluation frameworks with domain-specific metrics

Technical Collaboration

• Work closely with the AI/ML team to solve complex technical challenges
• Provide insights on architectural decisions and experimental design
• Share knowledge through technical discussions and code reviews
• Stay current with latest research and identify relevant advances for team adoption

Production Integration

• Deploy models into production environments with monitoring and continuous improvement
• Implement data preprocessing pipelines and augmentation strategies
• Optimize inference performance through quantization, pruning, and efficient architectures
• Build APIs and services for model deployment using FastAPI or similar frameworks

Required QualificationsEducation

• Currently pursuing or completed Master's/PhD in Computer Vision, AI/ML, Computer Science, or related field from premier institutions (IIT, IIIT, NIT, or equivalent)
• Strong academic record with focus on computer vision and deep learning coursework
• Active research profile with publications in top-tier conferences (CVPR, ICCV, ECCV, NeurIPS, ICML) or journals
• Thesis/research work demonstrating deep technical expertise in computer vision

Experience

• 2+ years of hands-on experience in computer vision and deep learning research/development
• Proven track record of implementing research papers and novel architectures from scratch
• Experience with real-world computer vision projects beyond academic coursework

Technical Expertise

Deep Learning & Computer Vision

• Expert-level proficiency in PyTorch (preferred) or TensorFlow
• Strong understanding of CNN architectures (ResNet, EfficientNet, DenseNet, etc.)
• Experience with Vision Transformers (ViT, Swin, DINO, etc.)
• Knowledge of attention mechanisms and self-attention for vision tasks
• Understanding of metric learning, contrastive learning, and embedding-based methods
• Experience with multi-modal learning and cross-modal fusion techniques

Computer Vision Fundamentals

• Deep understanding of image processing, filtering, and transformations
• Experience with object detection (YOLO, Faster R-CNN, DETR) and segmentation
• Knowledge of video analysis techniques and temporal modeling
• Familiarity with feature extraction and representation learning
• Understanding of data augmentation strategies and regularization techniques

Research & Implementation

• Ability to read, critically analyze, and implement research papers independently
• Experience with experimental design, hypothesis testing, and ablation studies
• Proficiency in experiment tracking tools (Weights & Biases, MLflow, TensorBoard)
• Strong mathematical foundation in linear algebra, optimization, and probability

Software Engineering