Software Development Engineer II
Summary
Build AI models and APIs for geospatial imagery, including LLMs, computer vision, and AI agents, using Python and ArcGIS tools.
Are you passionate about applying data science and artificial intelligence to solve some of the world’s biggest challenges?
Key Responsibilities
- Develop tools, APIs and pretrained models for DL and LLM workflows for imagery
- Develop foundation models for computer vision, language, location and multimodal data
- Develop AI Agents / Assistants to perform various imagery tasks
- Author and maintain samples showcasing AI/LLM applications in various ArcGIS platforms
- Perform comparative studies of various AI architectures and their applications to solve complex problems using imagery
Requirements
- 2+ years of experience with Python, in data science, deep learning, LLM
- Self-learner with extensive knowledge of machine learning, deep learning and LLM
- Expertise in one or more of the following areas: Traditional and deep learning-based computer vision techniques with the ability to develop deep learning models for computer vision tasks (image classification, object detection, semantic and instance segmentation, GANs, super-resolution, image inpainting, and more) Transformer models applied to computer vision and natural language processing Development of AI assistants / Agents to perform specific tasks Building from the scratch / finetuning, multi modal foundation models
- Bachelor's degree in computer science, engineering, or related disciplines from IITs and other top-tier engineering colleges
- Existing work authorization for UAE
- Experience applying deep learning to satellite imagery or geospatial datasets
- Familiarity with ArcGIS suite of products and concepts of GIS
- Practical experience applying deep learning concepts from theory to code, evidenced by at least one significant project (internship, research, or personal portfolio)
- Experience building and orchestrating multi-agent systems with tools like LangGraph, paired with expertise in model fine-tuning, safety controls, and data-driven evaluation techniques to validate agent performance.