Profile: Sr. AI Computer Vision Engineer-IT-Bengaluru
Experience:
5+ years of experience in Computer Vision, Deep Learning, and AI Strong experience building production-grade AI systems Deep understanding of computer vision and deep learning algorithms Strong Python and software engineering fundamentals Experience with end-to-end ML training and deployment pipelines Hands-on expertise with PyTorch, TensorFlow, and OpenCV Experience optimizing models for edge deployment
About the role:
Senior AI Computer Vision Engineer with deep expertise in computer vision, deep learning, 3D perception, and production AI systems. This is a 100% hands-on individual contributor role where you’ll design and deploy intelligent vision systems powering next-generation 3D experiences, spatial computing, and immersive AR/MR/XR applications.
You’ll work across computer vision, multimodal learning, edge AI deployment, 3D reconstruction, and ML infrastructure to build scalable, production-grade AI systems.
Roles:
1. Computer Vision & 3D Perception
Build advanced image understanding and scene analysis pipelines Develop 3D reconstruction and spatial understanding systems from multi-view inputs Design depth estimation, pose estimation, and camera calibration solutions Implement object detection, segmentation, tracking, and feature extraction models Build scene understanding and semantic mapping pipelines Develop image enhancement, preprocessing, and intelligent data workflows Create real-time perception systems for AR/MR/XR applications Enable ML-driven visual analytics and spatial intelligence
2. Deep Learning & AI Model Development
Design and optimize deep learning architectures for visual intelligence Train and fine-tune CNNs, transformers, MLLMs, and multimodal models Build perception, recognition, classification, and prediction systems Experiment with state-of-the-art AI approaches for visual computing Develop augmentation, evaluation, and continuous improvement pipelines Rapidly prototype using latest research and emerging frameworks
3. Edge AI & Production Deployment
Build end-to-end ML pipelines including ingestion, preparation, training, and deployment Deploy optimized models across edge environments and production systems Improve latency, throughput, and power efficiency for inference workloads Optimize models using TensorRT, CUDA, and hardware acceleration techniques Design scalable deployment architectures Implement monitoring, validation, and model lifecycle management
4. ML Infrastructure & System Engineering
Develop scalable AI services and modular deployment frameworks Build APIs and reusable AI components Implement CI/CD pipelines for ML workloads Containerize and orchestrate systems using modern infrastructure tooling Support annotation, validation, and production-quality operations Monitor model performance, drift, and reliability
5. Research & Innovation
Stay updated with advances in AI, computer vision, and 3D perception Prototype solutions using latest multimodal and vision technologies Evaluate and integrate open-source frameworks into production Contribute to architecture decisions and technical strategy Document learnings and share technical insights internally
Qualification:
Experience in product visualization, advertising, or fashion content Familiarity with AI video pipelines and multi-frame consistency Knowledge of LoRA training, fine-tuning, or custom model workflows Understanding of branding and visual identity systems Exposure to 3D workflows or hybrid AI + 3D pipelines
