Senior Full Stack Software Engineer

• Bachelor's degree in Computer Science, Software Engineering, or equivalent practical experience.
• 5+ years of experience building and deploying production-grade full-stack web applications.
• Proficiency in TypeScript and at least one modern frontend framework, with deep understanding of state management, reactive patterns, and asynchronous data flows.
• Strong backend development experience in Python, Java/Kotlin, or Go, with a solid grasp of foundational CS concepts in data structures, algorithms, and computer systems.
• Experience optimizing and scaling relational databases under production load including query optimization, indexing strategies, partitioning, and connection pooling.
• Strong async communication skills. You write clearly, document decisions well, and can collaborate effectively with a team operating primarily out of San Francisco across a significant time zone gap.
• Experience designing and building microservices architectures, including RESTful APIs, event-driven design patterns, and message queues.
• Hands-on experience with cloud-computing providers, including managed databases, compute, storage, and networking.
• Experience with Docker, containerized deployments, and CI/CD pipeline design.
• Experience with automated testing: Unit, integration, and end-to-end (Jest, Cypress, Playwright, or equivalent).
• Knowledge of logging and monitoring tools for troubleshooting production systems.
• Ability to operate with high autonomy in ambiguous, fast-moving environments — you'll be making architectural decisions, not just executing tickets.

• Experience with D3.js, Mapbox, Deck.gl http://Deck.gl, or WebGL for rendering complex geospatial or time-series visualizations.
• Experience building data-intensive applications that process and visualize large-scale time-series datasets (billions of records, sub-minute resolution).
• Experience with GIS data formats (shapefiles, GeoJSON, CIM models) and spatial analysis.
• Familiarity with ML model serving. Deploying and monitoring inference pipelines in production, model versioning, or feature stores.
• Experience with enterprise security architectures, compliance standards, or building multi-tenant SaaS platforms for regulated industries.
• Track record in high-growth or "zero-to-one" environments where you've built core infrastructure from scratch.
• Experience working with high-frequency time-series data including storage, retrieval, downsampling, and visualization of dense datasets at sub-hourly resolution.

Working hours: The team is distributed across India and California, so expect a few hours of evening overlap with US Pacific Time on most workdays.

• Design and implement high-performance web applications using TypeScript and React that visualize time-series demand forecasts, weather ensemble data, and grid analytics for utility operators.
• Build interactive geospatial visualization layers using libraries like Mapbox to render distribution network topology, feeder-level load profiles, and spatial weather overlays on utility grid maps.
• Develop real-time, dynamic dashboards for day-ahead and intraday energy demand forecasting, rendering large time-series datasets (billions of data points across thousands of feeders) with smooth, responsive interactions.
• Create map-based views that allow utility engineers to drill down from substation-level to individual distribution transformers, supporting bottleneck identification, fault isolation, and capacity planning.
• Implement reusable component libraries to ensure UI consistency across multiple utility-facing products. Collaborate closely with the Product team to translate Figma designs into powerful interfaces.
• Write comprehensive tests using frameworks like Jest to ensure reliability of mission-critical tools that utilities depend on for operational decisions.

• Design, build, and operate scalable microservices and REST APIs that power weather-driven electricity demand forecasting, grid simulation, and load-flow analytics.
• Build and maintain data ingestion pipelines that process high-frequency time-series data at scale, normalizing across inconsistent formats and time zones.
• Integrate backend services with ML inference pipelines, serving TiDE, transformer-based, and ensemble forecasting models that consume a rich set of feature covariates including weather, calendar effects, and grid state variables. Support model versioning, A/B testing, and automated retraining workflows.
• Build services that manage network metadata and grid topology, ingesting GIS shapefiles, CIM models, and utility asset registers to support load-flow simulations and network loss calculations at distribution scale.
• Develop and enforce secure, compliant data access frameworks for sensitive utility data, including role-based access controls (RBAC) and audit logging appropriate for enterprise utility clients.
• Design backend systems using event-driven architecture patterns and message queues to handle asynchronous processing of large-scale batch forecasting jobs and automated reporting workflows.
• Work with Cloud SQL (PostgreSQL) for relational data and appropriate NoSQL stores for high-throughput time-series ingestion. Optimize query performance as data volume scales across utility clients.

• Build and evolve CI/CD pipelines on Google Cloud Platform to reliably deploy data-intensive services, ML-backed APIs, and frontend applications.
• Implement production observability including structured logging, metrics dashboards, and alerting to detect and debug issues across data pipelines and forecasting services.
• Own production deployments and incident response for backend systems that utilities rely on for operational planning. Ensure high availability and graceful degradation.
• Containerize services with Docker, manage orchestration, and design deployment patterns that support multi-tenant utility environments with client-specific configurations.
• Continuously improve system scalability and reliability as Pravāh scales its customer base by 10x and data volumes by orders of magnitude.

Pravah is building the world's first foundation model of the electric grid. The world's most important physical industry is under unprecedented stress. We are using ML to build the 22nd-century grid. We are backed by Khosla Ventures, Pear VC, and Conviction.

We work directly with state-owned distribution and transmission electricity utilities to transform their decades of fragmented operational data into deployable, decision-grade intelligence. Our customers include some of the largest DISCOMs in the country, and our work spans demand forecasting (including renewable generation forecasting), weather forecasting, network mapping, and load flow analysis.