-@Balaraj__r-000000?style=for-the-badge&logo=x&logoColor=white){kind=icon}
class BalarajR:
role = "AI/ML Engineer | Edge AI Specialist | Distributed Systems Builder"
location = "Bengaluru, Karnataka, India 🇮🇳"
education = "PES University — B.Tech CSE (AI & ML), 2024–2028"
portfolio = "https://balaraj.me"
contact = "balarajr483@gmail.com"
core_domains = [
"Edge AI — Quantized on-device LLM inference (Gemma, Llama.cpp, GGUF)",
"Agentic Swarms & Multi-Agent Orchestration (LangGraph, Google ADK, Genkit)",
"Healthcare AI — Offline-first clinical triage & HIPAA-ready architectures",
"Agritech AI — Satellite earth intelligence (Sentinel-2) + Multimodal vision",
"Event-Driven Cloud Microservices (GCP Cloud Run, Functions, AlloyDB, Firebase)",
]
flagship_systems = {
"Darwin" : "Multi-agent AI executive board running 3-round adversarial debates",
"VaidyaOS" : "Edge AI clinical OS running quantized LLMs on offline Android devices",
"CareerLens" : "32-microservice AI career mapping platform — Google Gen AI National Winner 🏆",
"AgriSence" : "Precision agriculture OS serving 140M smallholder farmers — Inferentia Winner 🥇",
"TaskForze" : "Autonomous multi-agent swarm orchestration with dynamic self-healing loops",
}- AgriSence: The AI Operating System for India's 140M Smallholder Farmers — Jun 15, 2026
- Building Darwin: The AI Executive Board for Startup Founders — Jun 12, 2026
- TaskForze: Autonomous Agent Swarm Orchestration with Dynamic Replanning — Jun 10, 2026
- Building VaidyaOS: Offline Healthcare AI Using Edge AI — May 29, 2026
- How AgriSence Uses AI for Crop Disease Detection — May 28, 2026
📡 Updated automatically every 12 hours via GitHub Actions from
balaraj.me/rss.xml
graph TD
User["Client (Mobile / Web App)"] --> Gateway["API Gateway / Edge Router"]
subgraph MultiAgentEngine ["Darwin & TaskForze Multi-Agent Core"]
Gateway --> Supervisor["Lead Supervisor / Orchestrator Agent"]
Supervisor --> Agent1["Specialist Agent A (Financial / Clinical)"]
Supervisor --> Agent2["Specialist Agent B (Architecture / Tech)"]
Supervisor --> Agent3["Specialist Agent C (Risk / Validation)"]
Agent1 & Agent2 & Agent3 --> Consensus["Debate & Synthesis Engine"]
Consensus --> DeterministicRules["Deterministic Constraint Enforcement (Hard Vetoes)"]
end
subgraph EdgeInference ["VaidyaOS Offline Edge Layer"]
MobileClient["Android Device"] --> LocalGGUF["Quantized Gemma/Llama Model (llama.cpp)"]
LocalGGUF --> LocalStorage["Local SQLite + Offline Triage Engine"]
LocalStorage -.-> Sync["Async Cloud Sync on Connection"]
end
subgraph CloudInfra ["GCP Serverless Infrastructure"]
DeterministicRules --> CloudRun["Google Cloud Run / Cloud Functions"]
CloudRun --> VectorDB["AlloyDB + ScaNN Vector Search"]
CloudRun --> Firestore["Firebase Firestore (Live State)"]
end
| 🥇 Award | 🏛️ Organiser | 📅 Date |
|---|---|---|
| 🏆 National Winner — Google Gen AI Exchange Hackathon | Google Cloud × Hack2skill | 2025 |
| 🥇 1st Place — Inferentia 2.0 State Hackathon | PES University · AURA · AI&ML Dept | Sep 2025 |
| 🌟 Google APAC Finalist — Gen AI Academy APAC | Google Cloud × Hack2skill | 2026 |
| 🚀 Round 1 Finalist — Meta PyTorch OpenEnv Hackathon | Meta × OpenEnv × Scaler | Apr 2026 |
| 🛡️ AMD Slingshot Hackathon — CyberShield AI | AMD | 2025 |
Problem: Generic startup advice fails solo founders. They need an execution plan constrained by their actual technical skills, capital, and risk tolerance.
Darwin is a full-stack AI platform that acts as an AI-powered executive board. It builds a "Digital Twin" of the founder's constraints, then runs a 3-round debate among 5 specialized AI agents (CEO, CFO, CTO, CMO, CPO) to synthesize a final PROCEED/PIVOT/REJECT verdict with an execution blueprint.
- Architecture Deep Dive: Read on balaraj.me/blogs/building-darwin-ai-executive-board.
- Interactive Case Study: Read on balaraj.me/projects/darwin.
Problem: 65% of India's rural population lacks access to qualified doctors. Internet connectivity is intermittent. Clinical AI must work without the cloud.
VaidyaOS is an offline-first Edge AI operating system for healthcare accessibility. It runs quantized LLMs directly on Android devices — enabling clinical triage, symptom analysis, and multilingual medical guidance with zero internet dependency.
- Architecture Deep Dive: Read on balaraj.me/blogs/building-vaidyaos-offline-healthcare-ai-edge-ai.
- Interactive Case Study: Read on balaraj.me/projects/vaidyos.
Problem: Indian farmers lose 20–40% of crops annually to preventable diseases and poor advisory access. Agronomic advice is expensive, English-only, and internet-dependent.
AgriSence combines multimodal Gemini vision, satellite earth intelligence (Sentinel-2 and Landsat), and regional voice AI across 7 Indian languages to serve 140 million smallholder farming households.
- Architecture Deep Dive: Read on balaraj.me/blogs/agrisence-ai-agricultural-operating-system.
- Interactive Case Study: Read on balaraj.me/projects/agrisence.
- Architecture Deep Dive: Read on balaraj.me/blogs/event-driven-microservices-ai.
- Interactive Case Study: Read on balaraj.me/projects/career-lens.
- Architecture Deep Dive: Read on balaraj.me/blogs/taskforze-autonomous-agent-swarm-orchestration.
- Interactive Case Study: Read on balaraj.me/projects/taskforze.
Balaraj is ranked in Google Cloud Skills Gold League (6,527 points). Verified badges include:
- 🛡️ AI & Enterprise Data: Build AI Agents with Enterprise Databases • Prompt Design in Vertex AI • Prepare Data for ML APIs • Streaming Analytics into BigQuery
- ☁️ Serverless & Kubernetes: Serverless on Cloud Run • Cloud Run Functions (3 Ways) • Manage Kubernetes in Google Cloud (GKE) • Develop Serverless Apps with Firebase
- 🔐 Security & Infrastructure: Build a Secure Google Cloud Network • Implement Sensitive Data Protection • Create a Secure Data Lake
- 📜 Official Transcript: View Verified Transcript on Google Skills
"Build AI systems that support humans in critical decision-making — safely, transparently, and responsibly."
— Balaraj R | balaraj.me | Open to Collaborations 🤝