Pragnyan Ramtha

• Shipped a no-code AI agent platform from zero to production solo, connecting 1,000+ app connectors and orchestrating 20,000+ tools by building the full stack: agent engine, approval system, integrations, and no-code frontend.
• Enabled teams to hand off 20+ hours of weekly repeat work, agents run autonomously for hours to days across research, outreach, CRM, and cross-app workflows by designing a long-running orchestration system with approval-first safety controls.

• Next.js
• TypeScript
• AI Agents
• Full-Stack Development
• SaaS

• Built assistive software for blind learners from scratch, shipping a learning portal with screen-reader compatible workflows by engineering accessibility-first interfaces and AI-powered assistance tools.
• Delivered accessible class sessions for visually impaired students, adapting tools and workflows for screen-reader compatibility across real-time sessions by collaborating with educators and TEDx speakers to design inclusive learning experiences.

• Accessibility Engineering
• Accessibility
• Python
• Learning Portals
• AI Assistance

• Architected a GCP serverless backend achieving 99.9% uptime by leveraging Cloud Functions and Cloud Run for production-grade AI orchestration.
• Reduced inference latency by 50% across support workflows by engineering a Gemini API response system with optimized prompt caching.
• Boosted accuracy and trust by developing a RAG pipeline utilizing semantic search for real-time documentation retrieval and source attribution.

• Web Development
• Full-Stack Development
• GCP
• Gemini API
• RAG
• Python

• Researched world-model approaches for architecture workflows to generate CAD-style design outputs from spatial context and architect design intent.
• Prototyped ML pipelines that translated early architectural concepts into structured geometry, creating a faster path from design exploration to CAD handoff.
• Evaluated generated layouts against architectural constraints to improve reliability before model outputs were used in downstream design workflows.

• Artificial Intelligence
• World Models
• CAD Generation
• Machine Learning
• Python

• Built a no-code AI agent platform from scratch, solo. Agents connect to 1,000+ apps and run autonomous sessions for hours or days.
• Handles research, outreach, CRM updates, lead qualification, and cross-app workflows from plain English instructions.

• Next.js
• TypeScript
• AI Agents
• Integrations
• SaaS

• AIMO-3 Solver Medalist — fine-tuned Phi-4 (14B) on CoT and TiR datasets to optimize multi-step mathematical reasoning, achieving 90% accuracy on competition benchmarks rivaling 125B parameter models with a fraction of the compute.

• Phi-4
• Fine-tuning
• CoT/TiR
• PEFT

• Fine-tuned a Large Language model, leveraging PEFT (QLoRA) and contrastive learning on private conversational data to emulate personal response style.
• Implemented a siamese network architecture with cosine similarity loss, which improved semantic embeddings and achieved 92% accuracy in replicating my response style, a 28% improvement over baseline models.

• TensorFlow
• Python
• CUDA
• Transformers

Scaling Context Windows to Infinity: A Comprehensive Study of Position Encoding, Attention Mechanisms, Memory-Efficient Inference, and Context Reduction Techniques in Large Language Models 2026

Read paper Academia.edu

A comprehensive analysis of techniques for extending context windows in large language models, examining position encoding strategies, efficient attention mechanisms, and memory-optimized inference approaches to enable processing of arbitrarily long sequences.

Unlocking Societal Trends in Aadhaar Enrolment and Updates: Anomaly Detection and Fraud Risk Prediction 2026

Read paper Academia.edu

A data-driven approach to identify suspicious patterns in India's Aadhaar biometric identification system, utilizing machine learning for anomaly detection and fraud risk prediction in enrollment and update processes.

Speeding Up LLM Inference Using Quantum Computing Techniques 2026

In progress

Exploring quantum-inspired algorithms and quantum computational primitives to accelerate inference in large language models, investigating quantum annealing for attention mechanisms and variational quantum circuits for efficient token generation.