Role Overview :
We are seeking an accomplished and visionary AVP / VP Computer Vision to lead our AI and vision engineering initiatives in a fast-growing, VC-backed global AgTech company. The ideal candidate brings deep expertise in computer vision and AI, proven experience in deploying models into production (both edge and cloud), and strong leadership capabilities to scale innovative solutions in real-world scenarios.
Key Responsibilities :
- Strategic Leadership : Define and lead the companys computer vision roadmap aligned with product and business goals.
- Model Development & Deployment : Design, build, and deploy scalable computer vision solutions using cutting-edge deep learning frameworks (e.g., TensorFlow, PyTorch, OpenCV).
- Team Management : Build and mentor a high-performance CV / AI team. Foster collaboration and drive research-to-production pipelines.
- Edge & Cloud Integration : Architect and implement computer vision models optimized for edge devices and real-time cloud systems.
- Innovation & R&D : Explore emerging technologies including 3D computer vision, generative AI (GANs), LLMs, and lightweight vision models like SmolVLM.
- AI Infrastructure : Oversee the MLOps lifecycle including model training, versioning, deployment, and continuous inference monitoring.
- Cross-Functional Collaboration : Work with product, engineering, and business teams to translate complex AI solutions into business value.
- IP Development : Contribute to patents, technical publications, or white papers to strengthen the companys technical leadership.
Required Qualifications :
Bachelors, Masters, or PhD in Computer Science, AI, Computer Vision, or related field.10+ years of experience in computer vision or applied AI, with at least 3+ years in a leadership role.Expertise in deep learning frameworks (e.g., TensorFlow, PyTorch) and computer vision libraries (e.g., OpenCV).Proven track record in building and deploying computer vision models in production environments.Experience with edge computing (e.g., NVIDIA Jetson, Coral) and cloud platforms (AWS, GCP, Azure).Strong foundation in 3D vision, point cloud processing, depth estimation, or SLAM is a plus.Working knowledge of generative models (GANs, autoencoders), LLMs, and lightweight vision architectures (e.g., SmolVLM).Hands-on experience in MLOps, CI / CD for AI, model serving, and monitoring pipelines.Excellent problem-solving, team leadership, and stakeholder management skills.(ref : hirist.tech)