Meteorology / Atmospheric Science

Prayag.ai – Introduction

Prayag.ai is a fast-rising, AI-native startup with a projected turnover of ₹5 crore, founded by visionary technologists and first-principle thinkers committed to transforming intelligence into real-world impact. Though only six months old, Prayag has already delivered multiple domain-aware, production-grade AI systems that solve complex industry and societal problems.

Guided by senior advisors with decades of experience in enterprise software and scientific computing, Prayag combines youthful innovation with seasoned mentorship. Its agile team builds multimodal AI agents, document-intelligence pipelines, workflow copilots, and decision-support platforms using cutting-edge AI frameworks.

Prayag has rapidly executed intelligent AI pipelines that ingest, clean, organize, and analyze large-scale structured and unstructured datasets. These pipelines power domain-specific models that support decision-making across finance, climate science, public health, and governance. Notable solutions include anomaly-detection engines for financial audits, platforms that convert business plans into investor dashboards, knowledge-graph systems linking documents and codebases, multilingual voice-intelligence tools, and meeting-intelligence engines.

Prayag has built large-scale patent analytics frameworks, trade-analysis systems, and AI engines that assimilate physical models with real-time data—supporting applications such as rainfall forecasting and public-health monitoring. All solutions are built on transparent, auditable data lakehouses with integrated MLOps pipelines for training, validation, shadow testing, and retraining.

As an industry partner, Prayag.ai blends data engineering excellence, applied AI research, and rapid prototyping to deliver production-grade systems. Its strengths include FAIR/OGC-compliant data lakehouses, modular ETL pipelines, physics-informed neural network architectures, ensemble forecasting strategies, explainability layers, and full-stack dissemination frameworks with APIs and mobile dashboards. With proven deployments across banking, healthcare, education, real estate, and the public sector, Prayag.ai is positioned as an agile co-development partner for high-impact scientific and industrial initiatives.

Role: Tropical Meteorology & AI Integration Intern

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Role Overview

You will serve as the meteorological domain specialist in a cross-disciplinary team developing a high-resolution, physics-aware rainfall forecasting framework. Your expertise in atmospheric processes will guide the AI team in embedding correct physical behavior into neural operators and PINN-based architectures. While you will not train models directly, your insights will drive physics-aware model refinement, interpretation, and validation.

Key Responsibilities

Atmospheric Science & Data Interpretation

·      Analyze synoptic charts, monsoon dynamics, vertical motion fields, moisture transport, and convergence zones.

·      Interpret IMDAA reanalysis variables including CAPE, CIN, LCL, omega, moisture flux, RH/temperature profiles, vorticity, divergence, and pressure gradients.

·      Identify signatures of:

• convective bursts
• sea–land breeze interactions
• orographic uplift
• Bay of Bengal system influences
• tropical instability patterns

Supporting AI Training & Validation

• Provide physical constraints and scientifically valid thresholds for model development.
• Evaluate AI-generated predictions for physical consistency and meteorological realism.
• Explain failures such as missed convection or inaccurate moisture representation.
• Contribute to physics-aware validation metrics and model refinement cycles.

Required Qualifications

• Pursuing or completed B.Tech/M.Tech/M.Sc in Meteorology, Atmospheric Science, Earth Sciences, Climate Science, Physics, or related fields.
• Strong understanding of monsoon meteorology and tropical convection.
• Ability to interpret reanalysis, satellite, and observational datasets.

Preferred Skills

• Experience with xarray, MetPy, Cartopy, and NetCDF.
• Familiarity with IMDAA/ERA5, radar, or satellite datasets.
• Exposure to WRF, NCUM, or basic NWP concepts.

What You Will Gain

• Experience integrating atmospheric physics with AI-driven forecasting.
• Exposure to national-level nowcasting and prediction workflows.
• Collaboration with AI engineers developing scientific ML systems.