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rahulprasanth487/README.md

πŸ‘¨β€πŸ’» D Rahul Prasanth

Software Engineer Β· GenAI Platform Builder Β· Forward-Deployed Engineer

LinkedIn Email GitHub GCP Certified


πŸš€ About Me

I'm a Software Engineer, Bengaluru, working as a engineer on the GenAI platform β€” an enterprise-grade AI system.

I bridge the gap between users and product development, translating real-world enterprise needs into scalable AI solutions. My work spans building agentic AI workflows, implementing content guardrails with zero-downtime failover, and developing internal APIs for authentication, prompt orchestration, and response streaming.

Beyond engineering, I'm a published researcher, patent contributor, and passionate builder who loves crafting things at the intersection of AI, cloud, and impactful user experiences.


🧠 What I'm Working On

  • 🏦 GenAI Studio β€” Enterprise GenAI platform enabling agentic workflows and custom tool integrations
  • πŸ€– Agentic AI β€” Building systems where users can onboard custom agents, automate workflows, and integrate their own tools
  • πŸ“Š LLM Orchestration β€” Prompt engineering, response streaming, and multi-model evaluation pipelines

πŸ› οΈ Tech Stack

Languages

Python C++ JavaScript TypeScript

Web & Frameworks

React Node.js Flask HTML5 CSS3

Cloud & Databases

GCP Firebase MongoDB MySQL Firestore

AI / ML

LLMs Generative AI Machine Learning Agents

Tools & Platforms

Git GitHub VS Code Unity Vuforia


πŸ“š Research

"Attention-Based Rewards in LSTM for Detection of Stimming Behaviour in Autistic Children" Egyptian Informatics Journal Β· Elsevier Β· Published November 2025 πŸ”— View on ScienceDirect

Authors: J. Christina, J. Dhalia Sweetlin, D. Rahul Prasanth

Abstract: Autism Spectrum Disorder (ASD) is a neurological condition characterized by challenges in social communication and repetitive stimming behaviours such as arm flapping, head banging, and spinning. This study proposes a novel attention-based reward-driven LSTM architecture for detecting stimming behaviours from video data. The LSTM captures long-term temporal dependencies across frames, while the attention mechanism highlights the most relevant features β€” adapting to the diverse and individualized nature of ASD. This reward-based deep learning approach differentiates stimming behaviours between children with ASD and typically developing children, demonstrating significant improvement in action detection accuracy.

Keywords: Autism Spectrum Disorder LSTM Attention Mechanism Stimming Detection Deep Learning Computer Vision Healthcare AI


πŸŽ“ Education

B.Tech in Information Technology Madras Institute of Technology, Anna University, Chennai CGPA: 9.30 / 10.00 Β· 2020 – 2024


πŸ† Certifications

Certification Issuer Valid
Associate Cloud Engineer Google Cloud Oct 2024 – Oct 2027

🀝 Let's Connect

I'm always open to discussing GenAI, agentic systems, cloud architecture, or just great engineering ideas. Feel free to reach out!

LinkedIn Email


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