---
title: 'Forward Deployed Engineer at Pravāh'
canonical: 'https://feeny.ai/job/forward-deployed-engineer-pravah-delhi-rw9kapqfd9b0'
type: 'job'
last_seen: '2026-09-09'
---

# Forward Deployed Engineer at Pravāh

- **Company:** Pravāh
- **Location:** Delhi, India
- **Compensation:** $20k–$30k
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-05-17
- **Last confirmed live:** 2026-09-09
- **Apply:** https://jobs.ashbyhq.com/pravah/1e002f56-0b3c-4908-9bee-fb94215bfead

## Job description

Location: New Delhi/Vadodra/Jaipur/Vizag. 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 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.

## ABOUT THE ROLE

As a Forward Deployed Engineer at Pravah, you will be the bridge between our customers and our product. You will be embedded directly with DISCOMs across India to understand the realities of how the grid runs. Since we are an early stage startup, there are many situations where you will be expected to handle things which are beyond the scope of this JD, and your problem solving skills will be put to test. This is a role for someone who is equally comfortable writing Python on a flight, to drone mapping a substation, and presenting to Senior Executives and IAS officers. It is unusually broad: applied data work, large-scale data engineering, geospatial analysis, and high-trust customer engagement.

## WHAT YOU WILL DO

- Travel to DISCOM sites across India and embed with their engineering, planning, and operations teams to understand their data landscape and operational pain points.
- Acquire, clean, and structure large-scale operational datasets: SCADA streams, smart-meter telemetry, GIS networks, billing records, and weather feeds, often spanning multiple years and hundreds of millions of rows.
- Partner with the ML and engineering teams to tune and deploy models for demand forecasting, renewable generation forecasting, network mapping, and load flow analysis, owning the data side and feeding the model side.
- Architect and ship cloud-native data pipelines on GCP, alongside the engineering team, that productionise these models for the customer.
- Work alongside utility leadership on procurement, compliance, and rollout from initial pitch to live deployment.
- Identify product gaps from the field and feed them back to the core engineering team.

## WHAT WE’RE LOOKING FOR

- A degree in engineering, computer science, or a quantitative discipline. We care far more about your ability to ship than the name on your degree.
- Strong programming fundamentals in Python; comfort with SQL, pandas, and at least one ML framework (scikit-learn, XGBoost, PyTorch).
- Experience working with large, messy real-world datasets and the patience to figure out why they’re messy.
- Comfort with cloud infrastructure (GCP or AWS) and modern data tooling.
- Strong written and verbal communication.
- Willingness to travel within India to the DISCOM offices, often at short notice.
- A bias toward ownership. You lead and execute the task with minimal hand-holding.
- A strong problem-solving instinct, including using AI agents and modern tooling intelligently to multiply your output. (Bonus points for shipping fast. Negative points for letting an agent drop the production database).
- Chill, easygoing and a friendly personality. We value people who are genuinely easy to work with and fit well within a tight-knit team.

## NICE TO HAVE

- Prior exposure to the power, energy, or infrastructure sector.
- Experience with geospatial data (QGIS, GeoPandas, shapefiles, GDB).
- Experience with deploying, managing or building end-to-end software products at scale.

## WHAT YOU GET

- Direct, visible impact on India’s grid. The systems you build go live with real utilities serving millions of consumers.
- An unusually broad technical scope: data engineering, geospatial, ML collaboration, and infrastructure (frontend, backend, devops, etc), all in one role.
- Opportunity to work with founders and senior team members from Stanford, Yale, and leading technology companies.

## About Pravāh

## Company Overview
- **One-liner**: Pravāh builds foundational AI intelligence for the electric grid, helping utilities forecast demand, model grid constraints, and reduce operational risk in real-time.
- **Entity Type**: Private (Seed Stage)
- **Headquarters**: San Francisco, California, United States (with offices in New Delhi, India)
- **Founded**: 2025
- **Founders**: Mohak Mangal (CEO) and Dhruv Suri (CTO)

## Core Business
- **Primary industry**: AI-powered Grid Intelligence / Clean Energy Software
- **Target customers**: Electric utilities (B2B/Enterprise) in India, Germany, and the United States
- **Mission or purpose statement**: To build the foundational intelligence for the electric grid and solve "a problem that impacts billions" — making electricity cleaner, more affordable, and more reliable as extreme weather, EVs, and rooftop solar strain the grid.

## Products & Services
- **AI Engine for Grid Management**: A machine learning platform that gives utilities real-time understanding of grid behavior under stress, using graph neural networks, reinforcement learning, computer vision, and deep learning-based forecasting.
- **Probabilistic Grid Simulations with Reinforcement Learning**: Tests thousands of possible grid futures to identify risk and failure modes before they occur.
- **Mapping Grid Infrastructure Using Computer Vision**: Uses satellite and street-level imagery to map grid assets and rooftop solar, revealing blind spots in the distribution network.
- **Deep Learning-based Forecasting**: Models electricity demand and distributed generation across time horizons, capturing volatility that legacy methods miss.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed
- **Key Metrics**: Raised **$7M** (seed round); 13 employees; works with utilities serving tens of millions of consumers across three continents.
- **Notable Investors**: Khosla Ventures (Vinod Khosla), Pear VC (Mar Hershenson), and Conviction
- **Growth Signals**: Rapid headcount growth (+7.7% monthly on LinkedIn), traffic growth of +251.1% monthly to the website, deployed with utilities in India, Germany, and the US, and expanding across three continents.

## Competitive Advantages
- **Proprietary AI stack purpose-built for energy grids** — including PowerGNN, a topology-aware graph neural network designed specifically for electricity grids.
- **Full-stack grid intelligence** covering forecasting, constraint modelling, asset mapping, and probabilistic simulation in a single platform.
- **Real-world deployments** across three continents, including utilities serving tens of millions of consumers.
- **Strong founding team** with Stanford origins, backed by top-tier Silicon Valley VCs (Khosla Ventures and Pear VC).

## Strategic Focus
- Scaling deployments to more utilities globally, particularly in the US, India, and Europe.
- Deepening the AI/ML research edge — publishing in top venues (e.g., assessing global ML weather prediction models, graph neural networks for grids).
- Hiring across engineering, data science, and power systems roles to accelerate product development and customer implementation.

## Why Work Here
- **Mission-driven**: Founders describe this as "a problem that impacts billions" — working on making energy cleaner and more reliable in an era of climate stress.
- **Small, high-impact team**: Only 13 employees with a flat structure (43% are founders/founding team members) — meaning every hire has outsized ownership.
- **Research-forward culture**: The company publishes academic research and employs ML Weather Scientists, Staff ML Researchers, and Power Systems Engineers — a blend of hard science and engineering.
- **Flexible work**: Hybrid workspace; offices in San Francisco (HQ) and New Delhi. Stanford-founded with strong academic ties.
- **Strong investor backing**: Backed by Khosla Ventures and Pear VC, providing resources and credibility.
- **Notable perks**: The team wrote a detailed Notion document for candidates explaining "what we are building, what is at stake, and why we are so excited about this problem" — signaling a transparent, mission-oriented culture.

## Sources
1. [pravah.com](https://www.pravah.com/)
2. [pravah.com/our-team](https://www.pravah.com/our-team)
3. [linkedin.com/company/pravah-ai-energy](https://www.linkedin.com/company/pravah-ai-energy)
4. [jobs.ashbyhq.com/pravah](https://jobs.ashbyhq.com/pravah)
5. [builtin.com/company/prav-h](https://builtin.com/company/prav-h)

## Other roles at Pravāh

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