Location: Pune
Experience: 3+ years in AI/ML with focus on Computer Vision
Employment Type: Full-Time
Qualification: Bachelor’s degree in Engineering, Computer Science, AI, Data Science, or related field (Master’s preferred in AI/ML/Computer Vision)
Experience:
- Minimum 3+ years of experience in AI/ML with strong focus on Computer Vision.
- Hands-on experience in point cloud processing, LiDAR analytics, or 3D AI-based projects.
- Experience working with advanced vision architectures such as SAM, SuperPoint, Vision Transformers, or equivalent AI models preferred.
Requirements:
- Develop, train, and optimize AI models for point cloud data processing, segmentation, object detection, classification, and spatial intelligence applications.
- Work with advanced computer vision architectures such as SuperPoint, SAM (Segment Anything Model), Vision Transformers, and related deep learning models.
- Design and implement scalable pipelines for processing LiDAR and point cloud datasets including LAS/LAZ files, 3D Tiles, and related spatial data formats.
- Perform preprocessing, augmentation, normalization, annotation, and dataset preparation for large-scale 3D and multimodal datasets.
- Optimize AI models for accuracy, scalability, memory efficiency, and real-time or near real-time inference performance.
- Collaborate closely with GIS, 3D visualization, backend, and product teams to integrate AI models into production-grade applications and workflows.
- Evaluate model performance using metrics such as precision, recall, IoU, F1-score, and inference benchmarks, while continuously improving model quality through iterative experimentation.
- Work on multimodal AI solutions combining point clouds, images, videos, and geospatial datasets.
- Research, experiment with, and implement emerging AI/ML advancements in computer vision, generative AI, and 3D intelligence.
- Support model deployment, serving, monitoring, retraining, and lifecycle management through MLOps practices.
- Implement efficient data pipelines and workflows for handling large-scale spatial and visualization datasets.
- Contribute to AI architecture discussions, technical documentation, and reusable framework development.
- Ensure robustness, reliability, and maintainability of deployed AI systems and inference pipelines.
- Work closely with cross-functional engineering teams to troubleshoot model behavior, performance bottlenecks, and deployment challenges.
- Strong analytical thinking, debugging, and problem-solving capabilities.
- Ability to work independently as well as collaboratively in cross-functional teams.
- Excellent communication, collaboration, presentation, and stakeholder interaction skills.
- Proficiency in MS Office and enterprise collaboration tools.
Key Performance Indicators:
- Accuracy and effectiveness of AI models measured through precision, recall, IoU, F1-score, and related performance metrics.
- Performance and scalability of models on large-scale point cloud and 3D datasets.
- Reduction in model inference time, latency, and computational resource consumption.
- Successful deployment and integration of AI models into enterprise-grade production systems.
- Quality, efficiency, and reliability of data preprocessing, annotation, and training pipelines.
- Continuous improvement in model performance through experimentation, tuning, and optimization iterations.
- Stability, reliability, and maintainability of deployed AI solutions and inference services.
- Contribution to innovation and adoption of advanced AI techniques across projects.
Skills & Competencies:
- Strong experience in Computer Vision, Deep Learning, and AI model development.
- Hands-on expertise in point cloud processing, 3D data handling, and spatial intelligence applications.
- Familiarity with advanced AI models such as SuperPoint, SAM, Vision Transformers, and related architectures.
- Proficiency in AI/ML frameworks including PyTorch, TensorFlow, Open3D, and similar libraries.
- Experience working with LiDAR datasets, LAS/LAZ formats, 3D Tiles, and point cloud processing workflows.
- Strong understanding of 3D geometry, spatial transformations, coordinate systems, and visualization concepts.
- Knowledge of model optimization techniques including quantization, pruning, acceleration, and inference optimization.
- Experience with dataset creation, annotation tools, and labeling workflows for AI training pipelines.
- Familiarity with MLOps practices including model deployment, monitoring, retraining, and CI/CD for ML systems.
- Exposure to GIS, geospatial intelligence, or spatial analytics platforms is highly preferred.
- Strong analytical, debugging, troubleshooting, and problem-solving capabilities.
- Ability to collaborate effectively with cross-functional technical teams.
- Excellent communication, presentation, and collaboration skills.
Send your CVs to [email protected]
