AI technology, Analytical and Problem solving, Apache Kafka, C Programming, C++, Computer Vision (CV), Continuous Integration & Continuous Delivery - CI/CD, Deep Learning, Effective communication skills, English Proficiency, Github, Knowledge graph, Machine learning techniques, Neural Networks, Object detection, OpenCL, PyTorch, TensorFlow
We are looking for R&D minded Deep Learning Engineer, with demonstrable experience in Graph Neural Networks. The candidates should also have a solid background in Bayesian Learning. With this experience in both classical and deep learning approaches, the candidate will be in a position to create cutting edge Neural Reasoning solutions to understand construction sites from sensor data.
The candidate should also have Deep Learning experience in Computer Vision topics such as Object Detection, Instance Segmentation, Semantic Segmentation, Pose Estimation, Depth Estimation etc. Experience in more advanced learning methods such as Self-Supervision or Few Shot Learning would be a plus.
The candidate will be required to demonstrate the prototype on real data in real world conditions. We will provide the environment and tools through our platform to productionize it. We expect the candidate to have a balance of innovation and application perspectives. We will provide a unique opportunity to work on the 3rd generation AI and facilitate the candidate to make cutting edge contributions in the form of a product.
You will work closely with and learn from the Founder/CTO, Dr. Krishna Sridhar, former Program Head of Autonomous AI (autonomous driving) at Continental AG and who earlier worked as a Post-Doc AI Researcher at a DARPA Project(US Dept. of Defense).
Requirements
CONXAI is building an AI platform for the AEC industry to quickly and easily contextualize different types & formats of project lifecycle data from multiple sources, and transform them into actionable knowledge. Since its founding in 2021 in Germany, CONXAI has focussed on effortlessly equipping AEC teams with the data and knowledge they need to deliver higher performance through automated decision support, planning and workflows.