Overview

We work directly with state-owned distribution and transmission electricity utilities to transform their decades of fragmented operational data into deployable, decision-grade intelligence.

Responsibilities

• 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.
• 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.