Senior Full Stack Software Engineer
pravah · Remote
Experience: Senior
Senior Full Stack Software Engineer Overview About Pravāh Pravāh is an AI Lab building foundational intelligence for the electric grid. We apply modern machine learning to complex physical infrastructure problems spanning grid operations, weather, and geospatial systems. Our work sits at the intersection of computer vision, physical systems, and large-scale ML, with deployments across utilities in the United States and India. We leverage multimodal data including satellite imagery, LiDAR, and street-level data to build high-fidelity representations of grid assets and their surroundings. We are backed by Khosla Ventures, Pear VC, and Conviction - some of the most ambitious investors in Silicon Valley. More about who we are, what we are building, and why we are excited: www.pravah.com . The role As a Senior Full Stack Engineer, you will play a central role in designing, developing, and scaling of Pravāh's AI-driven grid intelligence platform end-to-end, from interactive frontends that make complex energy data actionable, to backend services that power real-time forecasting and grid analytics for some of India's largest utilities. You'll work across the entire stack: building high-performance web applications that visualize time-series and geospatial data, designing scalable APIs and data pipelines that integrate weather, demand, and grid topology datasets, and productionizing machine learning models in collaboration with our power systems and ML teams. You'll also shape our core infrastructure: CI/CD, observability, and deployment patterns. This is an early engineering role. You'll have a direct impact on a platform relied on by utilities across the globe. If you want to build critical infrastructure at the intersection of AI and energy, with real contracts, real data, and real-world consequences, this is the role. Key Responsibilities Frontend Development Design and implement high-performance web applications using TypeScript and React that visualize time-series dema