Prayag.ai is a fast-rising, 5 crore projected turnover, AI-native startup founded by a team of visionary technologists and first-principal thinkers with a shared conviction: to turn intelligence into impact. Though just 6 months old, Prayag has already demonstrated its ability to deliver domain-aware, production-grade AI systems that solve real problems Prayag.ai brings cutting-edge capabilities, the drive to experiment, and the ability to rapidly translate academic models into industry-grade prototypes — making it an ideal collaborator for ambitious, AI-enabled scientific initiatives. Prayag.ai is looking for full time Meteorology / Atmospheric Science Intern Role Overview You will serve as the meteorological domain expert in a cross-disciplinary team developing a high-resolution, physics-aware AI rainfall forecasting framework . Your primary role is to provide the physics insights, atmospheric constraints, and convection signatures that guide the AI team during model training, validation, and refinement cycles. You will not directly tune or train neural networks; instead, you enable the AI team to encode correct meteorological behaviour in neural operators and PINN-based architectures. Key Responsibilities: Atmospheric Science & Data Interpretation Analyze synoptic charts, monsoon structure, vertical motion fields, moisture dynamics, easterly waves, ISOs/BSISOs, and coastal convergence patterns. Interpret key IMDAA reanalysis variables: CAPE, CIN, LCL Vertical velocity (omega) Moisture flux & convergence Relative humidity & temperature profiles Vorticity, divergence, pressure gradients Identify signatures of: convective bursts sea–land breeze interactions orographic uplift rainfall Bay of Bengal system influences tropical instability patterns Supporting AI Training & Validation Provide physical constraints and scientifically valid thresholds that guide neural operator training. Evaluate AI model outputs for meteorological correctness , tropical realism , and physical consistency . Collaborate with the AI intern to iteratively improve model performance by explaining: why a prediction failed which atmospheric variables matter how convection behaved historically Contribute to the development of physics-aware validation metrics. Required Qualifications Pursuing or completed: M.Sc/M.Tech/B.Tech in Meteorology, Atmospheric Science, Earth Sciences, Climate Science, Geophysics, or Physics. Strong understanding of monsoon meteorology and tropical convection. Ability to interpret reanalysis and observational datasets. Preferred Skills Experience with: xarray, MetPy, Cartopy, NetCDF Familiarity with IMDAA, ERA5, satellite & radar datasets Exposure to WRF, NCUM, or NWP concepts What You Will Gain Work with an AI–meteorology hybrid forecasting team. Contribute to a national-level rainfall nowcasting Learn how AI models integrate atmospheric physics for real-world impact forecasting.
1. 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 system. 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: AI Engineer – Generative AI & Machine Learning Location: Chennai Experience: 3+ Years Team: Works jointly with Data Scientists and Software Engineering Teams Project: Development of enterprise-grade Generative AI and ML solutions Role Overview You will serve as the AI/ML specialist in a cross-functional team building large-scale generative AI and machine learning solutions for enterprise applications. Your expertise in LLMs, embeddings, model fine-tuning, and scalable ML systems will shape the core intelligence across multiple product lines. You will be responsible for designing, training, and optimizing AI models, integrating them into applications, and ensuring they perform reliably in real-world scenarios. While domain experts guide business requirements, you will lead the full technical execution of AI/ML pipelines. Key Responsibilities Generative AI Development & Model Engineering Build and optimize LLM-based systems using open-source models (Llama, Mistral, Qwen, etc.). Perform fine-tuning, LoRA, RAG optimization, prompt engineering, and model alignment. Develop custom pipelines for: text generation summarization semantic search document intelligence agent-based workflows code generation & automation Integrate external APIs like OpenAI, Anthropic, Azure OpenAI, and HuggingFace models. Machine Learning & Predictive Modelling Design and train supervised and unsupervised ML models for classification, regression, and clustering. Build scalable pipelines for data cleaning, vectorization, feature engineering, and model evaluation. Implement deep learning architectures (Transformers, CNNs, RNNs) for domain-specific business problems. Optimize models for latency, accuracy, and cost for production environments. RAG & Knowledge Engineering Build Search + GenAI pipelines using vector databases (FAISS, Pinecone, Chroma, Weaviate). Design chunking strategies, embedding pipelines, and retrieval tuning. Implement guardrails, grounding techniques, and hallucination-reduction workflows. Enable domain-specific knowledge integration into LLMs. MLOps & Deployment Develop reproducible ML training pipelines using MLflow, Weights & Biases, or equivalent tools. Containerize models with Docker and deploy on AWS/GCP/Azure. Optimize inference using quantization, distillation, and GPU acceleration. Ensure monitoring, logging, and model governance in production. Required Qualifications B.Tech/M.Tech in Computer Science, AI/ML, Data Science , or related fields. 3+ years of hands-on experience building and deploying ML and GenAI solutions. Strong proficiency in Python , PyTorch/TensorFlow , and modern ML frameworks. Experience working with LLMs, embeddings, transformers, and vector databases. Solid understanding of ML fundamentals, evaluation metrics, and model lifecycle management. Preferred Skills Experience with LangChain, LlamaIndex, or other orchestration frameworks. Familiarity with cloud AI stacks (AWS Sagemaker, Azure AI, GCP Vertex). Exposure to toolings like FastAPI, Kafka, Redis, Elasticsearch. Understanding of responsible AI: bias mitigation, alignment, safety best practices. Experience with multimodal models (vision-language, speech models) is a plus. What You Will Gain Opportunity to build cutting-edge GenAI products used by enterprises. Experience working on end-to-end AI systems from ideation to production deployment. Collaboration with domain experts across industries to solve real-world problems with AI. Deep exposure to the latest innovations in LLMs, multimodal AI, and enterprise automation.
1. 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 Location: Chennai (Hybrid/Remote) Duration: 3–6 months Team: Works jointly with the AI/ML Intern Project: AI-driven Rainfall Forecasting for South Peninsular India 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.