/*
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* ║ PRAJWAL N // prajwal.conf v2.0 ║
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*/
struct Engineer {
name : "Prajwal N",
location : "Coimbatore, India",
education : "B.Tech AI & Data Science @ SNS College | CGPA: 8.50",
roles: [
"AI Engineer Intern @ InFynd [Feb 2026 – Present]",
"Chief Architect @ EduSpine [Jan 2026 – Present]",
"Founder & Lead Engineer @ Buildicy [buildicy.com]",
"Freelance Tech Consultant [Jun 2025 – Present]",
],
currently_shipping: [
"Agentic AI systems with tool use, memory, and multi-step reasoning",
"LLM inference optimization: quantization + batching + cost-aware SLOs",
"Multi-agent orchestration with LangGraph + CrewAI in production",
"Fault-tolerant distributed systems: Raft consensus, gRPC, Kafka",
],
proof_of_work: {
distributed : "15K+ ops/sec | Go + Raft + gRPC | 5-node cluster",
ai_infra : "40–60% latency cut | vLLM + semantic caching + async",
backend : "500+ concurrent users | 99.9% uptime | EduSpine",
observability: "LangFuse + MLflow pipelines — every token traced",
wins : "12x National Winner | 1st/350+ teams @ Promptforge",
},
}
impl Engineer {
fn philosophy(&self) -> &str {
"I don't optimize for interviews. I optimize for production."
}
}
Buildicy is my engineering studio — the place where ambitious startups and enterprises come when off-the-shelf solutions aren't enough.
| 🤖 GenAI & Agent Systems | ⚙️ Distributed Backend | 📊 MLOps & Data Infra | 🚀 Full-Stack Delivery |
|---|---|---|---|
| Multi-Agent Orchestration | Go · gRPC · Kafka | LangFuse · MLflow · RAGAS | React · Node.js · AWS |
| RAG Pipelines (Production) | Redis · Microservices | Neo4j · Vector DBs | Docker · K8s · CI/CD |
| LangChain · LangGraph | Raft Consensus Systems | Microsoft Fabric · Spark | FastAPI · Flask · REST |
| vLLM · Quantization | Load Balancing · K8s | Feature Stores · ETL | Terraform · CloudFormation |
15+ production systems · 100% on-time delivery · Real clients · Real infrastructure
| 🔧 System | 🏗️ Stack | 📈 Production Result |
|---|---|---|
| Distributed KV Store | Go Raft gRPC 5-node |
⚡ 15K+ ops/sec · auto failover · partition recovery |
| NeuralStream RAG Infra | vLLM FAISS AsyncIO LangChain |
🚀 40–60% latency reduction · 10K+ req/day |
| R²-RAG Exam Guide | LangGraph FAISS LangFuse OpenAI |
🎯 30% hallucination drop · traced + evaluated |
| EduSpine Platform | Flask Redis AWS EC2/S3 MongoDB |
🔒 500+ concurrent · 99.9% uptime · 40% API cut |
| Texopia NLP SaaS | Node.js MongoDB Docker Nginx |
📊 15K chars/req · <200ms · 92% sentiment acc. |
| Skin Lesion Classifier | ResNet50 TensorFlow OpenCV Flask |
🔬 90%+ accuracy · real-time inference API |
| Federated Learning Board | TensorFlow.js WebSockets React |
📡 50+ edge devices · zero data centralization |
| Captsee Web Perf Eng. | SEO Lazy Load Asset Pipeline |
📈 GTMetrix 60 → 95 · +25% user retention |
This is where most engineers stop. I go deeper.
┌─────────────────────────────────────────────────────────────────────┐
│ MY GENAI PRODUCTION STACK │
├─────────────────┬───────────────────────────────────────────────────┤
│ ORCHESTRATION │ LangChain · LangGraph · LlamaIndex · CrewAI │
│ AGENT SYSTEMS │ Multi-Agent · Tool Use · Memory · Planning │
│ RAG PATTERNS │ Hybrid Search · R²-RAG · Graph RAG · HyPE │
│ VECTOR STORES │ FAISS · Pinecone · Chroma · Weaviate · PGVector │
│ LLM SERVING │ vLLM · Ollama · OpenAI API · HuggingFace TGI │
│ OPTIMIZATION │ GGUF Quantization · AWQ · GPTQ · LoRA · QLoRA │
│ OBSERVABILITY │ LangFuse · MLflow · RAGAS · TruLens · Weights&B │
│ FINE-TUNING │ PeFT · LoRA · RLHF · DPO · Instruction Tuning │
│ GRAPH AI │ Neo4j · LangGraph · Knowledge Graphs · GraphRAG │
│ EVALUATION │ RAGAS · DeepEval · LangFuse Evals · Custom Bench │
└─────────────────┴───────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────────────┐
│ DISTRIBUTED SYSTEMS TOOLKIT │
├──────────────────┬───────────────────────────────────────────────────┤
│ CONSENSUS │ Raft · Paxos (theory) · Leader Election │
│ MESSAGING │ Apache Kafka · gRPC · WebSockets · REST │
│ CACHING │ Redis · Semantic Cache · CDN · Write-Through │
│ DATABASES │ PostgreSQL · MongoDB · Neo4j · Vector DBs │
│ INFRA AS CODE │ Terraform · Docker · Kubernetes · GitHub Actions │
│ CLOUD │ AWS (EC2/S3/Lambda) · GCP Vertex AI · Azure │
│ OBSERVABILITY │ Prometheus · Grafana · MLflow · LangFuse │
│ PATTERNS │ CQRS · Event Sourcing · Saga · Circuit Breaker │
│ PERFORMANCE │ Load Balancing · Connection Pooling · Async I/O │
└──────────────────┴───────────────────────────────────────────────────┘
⚙️ Distributed Key-Value Store — Go · Raft · gRPC · Fault Injection
┌─────────────────────────────────────────────────────────────────┐
│ TYPE : Fault-Tolerant Replicated Storage System │
│ CONSENSUS : Raft — Leader Election, Log Replication │
│ TRANSPORT : gRPC with Protocol Buffers (zero-copy) │
│ CLUSTER : 5-node deployment with chaos/fault injection │
│ THROUGHPUT : 10,000–15,000+ ops/sec under adversarial load │
│ CONSISTENCY : Strong — no dirty reads, no split-brain │
│ RESILIENCE : Auto leader failover + partition recovery │
└─────────────────────────────────────────────────────────────────┘
Engineering insight: Most engineers use distributed databases. I know how they work — commit indices, log entries, term numbers, quorum math. Built this to prove systems-level thinking, not just framework fluency.
🧠 NeuralStream — Production LLM Inference + Multi-Agent RAG Platform
┌─────────────────────────────────────────────────────────────────┐
│ TRAFFIC : 10,000+ daily requests │
│ LATENCY WIN : 40–60% reduction via semantic caching + async │
│ ACCURACY : -30% hallucination via R²-RAG + LangGraph flow │
│ AGENTS : Multi-agent orchestration with CrewAI + LangGraph│
│ OBSERV. : Full LangFuse tracing — every token, every call │
│ EVAL : RAGAS pipeline for continuous quality monitoring │
│ SERVING : vLLM + quantization (GGUF/AWQ) for cost control │
│ VECTOR DB : FAISS + Pinecone hybrid search │
└─────────────────────────────────────────────────────────────────┘
Engineering insight: Anyone can wire up LangChain. Building RAG that's fast, cheap, measurably accurate, and fully observable in production is a fundamentally different problem. I've solved it.
🕸️ GraphRAG Knowledge System — Neo4j · LangGraph · Graph Embeddings
┌─────────────────────────────────────────────────────────────────┐
│ ARCHITECTURE: Graph-augmented RAG with entity relationship maps│
│ DB : Neo4j with vector + graph hybrid queries │
│ FRAMEWORK : LangGraph for stateful multi-hop retrieval │
│ ADVANTAGE : Captures relationships flat vector search misses │
│ CERT : Neo4j Graph Data Science Certified │
└─────────────────────────────────────────────────────────────────┘
📦 Texopia — High-Throughput NLP SaaS Platform
┌─────────────────────────────────────────────────────────────────┐
│ SCALE : 100+ concurrent users | 15K chars/request │
│ LATENCY : <200ms P99 end-to-end │
│ ACCURACY : 92% sentiment classification │
│ AUTH : JWT + API gateway rate-limiting │
│ INFRA : Docker · Nginx LB · GitHub Actions CI/CD │
│ MONITORING : Custom metrics + alerting pipeline │
└─────────────────────────────────────────────────────────────────┘
📡 Federated Learning Dashboard — TensorFlow.js · WebSockets · React
┌─────────────────────────────────────────────────────────────────┐
│ SCALE : 50+ decentralized edge devices │
│ PRIVACY : Zero data centralization — full on-device train │
│ STREAMING : WebSocket push — no polling, real-time state │
│ FRONTEND : Async state machine — zero UI blocking │
│ IMPACT : Privacy-preserving ML for sensitive data domains │
└─────────────────────────────────────────────────────────────────┘
🔬 AI Skin Lesion Classifier — ResNet50 · TensorFlow · OpenCV · Flask
┌─────────────────────────────────────────────────────────────────┐
│ ACCURACY : 90%+ on ISIC benchmark (Transfer Learning) │
│ BACKBONE : ResNet50 fine-tuned with domain augmentation │
│ INFERENCE : Real-time Flask API + OpenCV preprocessing │
│ TECHNIQUES : Rotation · Scaling · Flipping for class balance │
│ DEPLOYMENT : Scalable REST API — single-call medical inference │
└─────────────────────────────────────────────────────────────────┘
╔═══════════════════════════════════════════════════════════════════════╗
║ HALL OF RECORDS ║
╠═══════════════════════════════════════════════════════════════════════╣
║ ║
║ 🥇 1ST PLACE National Promptforge [ 1 of 350+ teams ] ║
║ 🥈 RUNNER-UP National Datafest [ Top 2 Nationally ]║
║ 🏆 4× WINNER National Paper Presentations ║
║ 🏆 12× TOTAL National-Level Tech Events ║
║ ║
╠═══════════════════════════════════════════════════════════════════════╣
║ ║
║ 👨💻 150+ LeetCode Problems Solved (Active daily) ║
║ 👨🏫 20+ Engineering Students Mentored (DSA + Architecture) ║
║ 📦 15+ Production AI Applications Shipped ║
║ 🔬 15+ Interactive DSA/Career Sessions Conducted ║
║ 🏢 1 Company Founded → Buildicy [buildicy.com] ║
║ ║
╚═══════════════════════════════════════════════════════════════════════╝
| 🏢 Issuer | 📋 Certification | 📅 |
|---|---|---|
 |
Fabric Data Science & End-to-End Analytics — 3 Module Track | 2025 |
 |
Graph Data Science & RAG Certified Professional | 2025 |
 |
Generative AI Fundamentals + Databricks Lakehouse Platform | 2025 |
 |
Generative AI · Image Generation · Responsible AI · Cloud Native | 2024–25 |
 |
Introduction to Decoupled Applications | 2025 |
 |
Enterprise Design Thinking Practitioner & Co-Creator | 2023 |
 |
ML & AI · Ethical Hacking · React.js · C Programming · Power BI | 2024 |
Consistent daily practice · DSA fundamentals → system design thinking · First principles over memorization
2026 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
├── 🔬 AI Engineer Intern @ InFynd [Feb 2026 – Present]
│ └─ Agentic AI · Multi-Agent Systems · Production NLP
│
├── 🏗️ Chief Architect @ EduSpine [Jan 2026 – Present]
│ └─ Backend Architecture · 500+ Users · 99.9% Uptime
│
2025 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
├── 🧊 Founded Buildicy [2025 – Present]
│ └─ Elite AI & Engineering Studio → buildicy.com
│
├── 💻 Freelance Tech Consultant [Jun 2025 – Present]
│ └─ 15+ Apps · React · Flask · MERN · AI Integration
│
├── 🏎️ Tech Lead @ EduSpine [Sep 2025 – Jan 2026]
│ └─ Agile SDLC · Microservices · Mentored 20+ Engineers
│
├── 📊 Data Analytics Intern @ Deloitte [Jun 2025 – Jul 2025]
│ └─ 50K+ Data Points · Tableau Dashboards · Fraud Detection
│
├── 🌐 Web Dev & SEO Intern @ Captsee [Jun 2025 – Jul 2025]
│ └─ GTMetrix 60 → 95 · +25% User Retention
│
├── 👨🏫 LeetCode Championship Mentor [Jun 2025 – Jul 2025]
│ └─ 10+ Students · 15+ DSA Sessions · Interview Prep
│
2024 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
└── 🛠️ Full Stack Developer Intern @ Webgen [Jul 2024 – Aug 2024]
└─ MERN Stack · 5+ REST APIs · CI/CD · 90% Faster Deployments
def why_hire_prajwal():
return {
"ships_to" : "production, not just GitHub",
"debugs_from" : "user impact → system root cause",
"builds" : "infra that handles real traffic, real users, real costs",
"proven_by" : "12x national wins, 15+ shipped systems, 0 missed deadlines",
"currently" : "AI Engineer @ InFynd + Founder @ Buildicy",
"open_to" : ["Full-Time Roles", "Research Collab", "AI Infra Consulting"],
"contact" : "prajwalgenious@gmail.com"
}
"The best engineers aren't the ones who know the most tools. They're the ones who know which tool to use, when to use it, and how to make it survive Monday morning in production."
— Prajwal N