onsite
Computer vision engineer
Computer Vision Engineer
The Computer Vision Engineer will join the AI/ML team to develop and implement state-of-the-art computer vision solutions for specialized detection and analysis systems. This role involves designing and implementing novel architectures, building end-to-end deep learning pipelines, and deploying models into production environments, focusing on deep learning, forensic AI, and multi-modal analysis.
About the role
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 Responsibilities
Research & 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 Qualifications
Education
- 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
- Proficient in Python with clean, modular coding practices
- Experience with OpenCV, torchvision, PIL/Pillow, and other CV libraries
- Knowledge of version control (Git) and collaborative development workflows
- Familiarity with Docker and containerization
- Experience with large-scale dataset handling and efficient data loading
Preferred Qualifications
- Publications in top-tier CV/ML conferences or journals (CVPR, ICCV, ECCV, NeurIPS, ICML, AAAI, etc.)
- Experience with forensic analysis, anomaly detection, or media authenticity verification
- Knowledge of generative models (GANs, VAEs, Diffusion Models)
- Understanding of adversarial robustness and model security
- Experience with 3D vision, depth estimation, or multi-view geometry
- Familiarity with signal processing for audio/visual analysis
- Background in image quality assessment or artifact detection
- Experience with few-shot learning, open-set recognition, or domain adaptation
- Knowledge of model compression and efficient architectures
- Contributions to open-source computer vision projects
- Experience with cloud platforms (AWS, GCP, Azure) for ML workloads
What We're Looking For
- Research Mindset: Strong analytical thinking with ability to formulate and test hypotheses rigorously
- Technical Excellence: Deep understanding of computer vision theory and modern deep learning
- Implementation Skills: Ability to quickly prototype ideas and translate research into working code
- Problem Solver: Creative approach to solving novel and ambiguous technical challenges
- Self-Motivated: Takes initiative in exploring new techniques and driving projects forward
- Collaborative: Excellent communication skills with ability to explain complex concepts clearly
- Detail-Oriented: Commitment to thorough experimentation, validation, and documentation
- Continuous Learner: Passion for staying current with rapidly evolving CV/ML research
What We Offer
- Opportunity to work on cutting-edge computer vision research with real-world applications
- Collaborative environment with focus on innovation and technical growth
- Exposure to production ML systems and end-to-end project ownership
- Flexibility to pursue research interests aligned with project goals
- Competitive compensation package