Pragnyan Ramtha

about me.

Results-driven AI/ML Engineer specializing in Large Language Model (LLM) fine-tuning and AI system design. I design maintainable, production‑grade AI systems and can comfortably work with deep cloud infrastructure. I learn new tools fast and use AI as a force‑multiplier in my coding, designing, and research loops, which lets me move much faster while keeping systems reliable.

experience.

• Special Education Specialist Remote

  at, Learnable India

  Apr 2026 - Present

  • Researched accessibility gaps by teaching and mentoring visually impaired students, then adapted class workflows to make learning delivery more usable for blind learners.
  • Built Learnable India's previously non-existent digital infrastructure by shipping a learning portal and AI assistance workflows, creating a foundation for student support and class delivery.
  • Collaborated with TEDx speakers, educators, and internal teams to turn expert sessions into accessible learning experiences for students with diverse learning needs.

  • Special Education
  • Accessibility
  • Python
  • Learning Portals
  • AI Assistance

• Agentic AI Developer Hyderabad, Telangana - Remote

  at, Reputation Dao

  Aug 2025 - Jan 2026

  • 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

• Machine Learning Intern Greater Hyderabad Area - Remote

  at, Six Axis Studios

  Feb 2025 - May 2025

  • 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

projects.

• AIMO-3: Efficient Reasoning via LLM Fine-Tuning

  View Project

  • Fine-tuned Phi-4 (14B) on CoT and TiR datasets to optimize multi-step problem solving and tool-use efficiency.
  • Achieved 90% accuracy on reasoning benchmarks, rivaling 125B parameter models while utilizing significantly fewer compute resources.

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

• Personality Clone

  View Project

  • 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

• Autopilot

  View Project

  • Engineered an AI-driven OS automation system leveraging function calling and tool-use paradigms to execute complex natural language tasks to achieve low-level automation.
  • Built a Reasoning + Acting agent framework with command sandboxing, reducing execution errors and achieving 45% faster task completion than manual workflows.

  • Python
  • LLM Agents
  • APIs

blogs.

• How I Built a Custom Harness to Beat the TerminalBench Leaderboard — Using GLMApr 2026

  Read full post 13 min read
  • The harness I built around GLM for TerminalBench, covering entry-point discovery, enforced planning state, trace retention, and parallel subagents.
  • Why the leaderboard data convinced me that harness engineering can close the gap between a mid-tier model and frontier agents.
  • TerminalBench
  • GLM
  • Agent Harnesses
  • Benchmarking
  • Tool Use

• Why OpenAI Sent Me $500 for a Research ProjectApr 2026

  Read full post 8 min read
  • How I built XSA4 + EMA + GPTQ-Int6 to place top-5 globally in OpenAI's Parameter Golf challenge.
  • Explaining bits-per-byte, Cross-Sparse Attention, EMA smoothing, and what GPTQ actually does under the hood.
  • Parameter Golf
  • OpenAI
  • Cross-Sparse Attention
  • GPTQ
  • Model Compression

• How I Reached #1 on ARC-AGI-2Apr 2026

  Read full post 7 min read
  • How I adapted my parallel agent setup from AIMO3 to hit the top of the ARC Prize 2026 leaderboard.
  • I used per-puzzle test-time training, a heavily restricted DFS beam search, and an augmented re-scoring trick to make it work.
  • ARC-AGI-2
  • Test-Time Training
  • Qwen
  • Kaggle
  • Reasoning

research papers.

• 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

  Read paper Under Research

  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.

technical skills.

• Languages:

  Python, C++, Bash, SQL, TypeScript

• AI/ML Tools:

  PyTorch, Transformers, Unsloth, NumPy, Pandas, Scikit-learn, PEFT/QLoRA, CUDA

• Infrastructure:

  GCP, Azure, Docker, Linux (Arch), Git

• Certifications:

  Machine learning certification (Stanford), CS50: comp. Sci. (Harvard University)

• Achievements:

  • Artificial Intelligence Mathematical Olympiad (AIMO) Silver Medalist
  • Authored 2 Research Papers on Modern AI Optimization Techniques
  • Winner, IEEE Summer of Code (IEEESOC) Hackathon 2025
  • Winner, Empathy Encryption Hackathon 2025
  • Winner, Daydream Hyderabad @ Hackclub 2025
  • Top 0.5% Finalist, Shell AI Hackathon 2025

• Developer Tools:

  uv, Neovim, Arch Linux