Forward Deployed Engineer at Pravāh (Delhi, India)Forward Deployed Engineer

Location: New Delhi/Vadodra/Jaipur/Vizag. We are hiring for 3 positions.

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.

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.

• 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.

• 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.

• 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.

• 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.