Associate

Location: New Delhi (primary). Other locations: Vadodara, Jaipur, and Visakhapatnam. We are hiring for 3 positions.

About Pravah

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 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.

About the Role

Our product will work only if it is being adopted by the customer and if it solving their biggest problems. You will spend weeks at a time embedded at customer sites, watching senior people use the platform in real time, capturing every point of friction, and translating it into precise, prioritised feedback for our engineers. You will sit in on demos to customer leadership, run 1:1 sessions with relevant stakeholders, and build the trust to ensure our engagement is successful.

This is a role for someone who can hold a technical conversation about power flow with an engineer (if you do not have this knowledge, it's fine. We would want you to learn quickly) and have the ability to present to a senior decision-maker like an IAS officer.

What You Will Do

• Deploy on site with customer utilities for extended stretches. You will work out of their offices.

• Sit alongside Directors, CGMs, and engineering teams as they use the platform. Watch where they hesitate and record it all.

• Produce structured client visit notes that separate UI/UX issues, broken functionality, and flawed analytical logic, with clear priority and enough specificity for a software engineer.

• Own the feedback loop with engineering. Push back when the field tells us something the roadmap doesn't reflect, and close the loop with the customer when a new feature ships.

• Map the customer organisation: who signs, who influences, who resents whom, who is up for transfer, which consultants are already embedded, and where the real veto sits.

• Present to senior leadership, including Chairmen, Directors, and IAS officers. Run demos, defend our methodology, and know when to concede a point and take it back to the team.

• Chase down validation. Get customers to test our outputs against their own data, and turn that into third-party proof of value that protects the engagement through leadership changes.

• Surface new opportunities from the field: adjacent products, pilot openings, integration requirements with the customer's existing platforms.

What We're Looking For

• 2-5 years at a top-tier consulting firm, a research or policy institution, a development finance or multilateral organisation, or a customer-facing role at a high-growth startup. Consulting backgrounds map unusually well here.

• Exceptional written communication. Your feedback documents are the primary interface between the customer and our engineers, and vague notes cost us weeks.

• Enough technical curiosity to get into the substance. You do not need to be an engineer, but you need to understand what a metric means, why a customer distrusts it, and be able to explain the difference to both sides.

• A bias toward ownership.

• Fluency with AI tooling as a force multiplier.

Nice to Have

• Prior exposure to the power, energy, or infrastructure sector, especially state utilities.

• Working comfort with data.

• Regional language ability, particularly in states where we are deployed.

What You Get

• Direct, visible impact on India's grid. The adoption you drive puts systems live with utilities serving millions of consumers.

• A front-row seat to how the country's power system actually runs.

• Opportunity to work with talented researchers from IIT, Stanford, etc. who have spent significant time working at the frontier of weather, power systems, and machine learning.