name: "Dama Sri Ram"
role: "AI Systems Engineer + Backend Architect"
location: "Punjab, India 🇮🇳"
age: 20
education: "LPU • 2nd Year CSE"
alias: "Friday" # My AI assistant codename
status: "MAANG or bust. No Plan B."
competitive_edge:
✓ Ships production AI systems at MAANG quality
✓ 1450+ DSA problems while building real products
✓ AI-native: 5x faster dev with Claude/Cursor
✓ Beat 550 teams at IIT Roorkee E-Summit 2026
✓ Selected by T-Hub (Top 1% nationally)
✓ 15+ merged PRs in production codebases
mission: |
100x my life using AI as a force multiplier.
Make my farmer father proud.
Crack MAANG. Build systems that matter.
Prove elite engineers come from anywhere. |
|
 ⚡ Rapid Prototyping Claude Code + Cursor for lightning-fast development cycles |
 🎯 Production Grade AI-assisted prototyping, human-optimized production code |
 🧠 Hybrid Intelligence LLM reasoning + deep CS fundamentals = unstoppable |
 🚀 Real Systems Production AI systems, not toy projects or demos |
// Most devs: Build → Test → Debug → Ship (8 weeks)
// Me: AI-assist → Validate → Optimize → Ship (3 weeks)
const ghostcutProject = {
traditionalTime: "8 weeks",
withAI: "3 weeks",
speedup: "2.6x faster",
quality: "Same MAANG standards",
result: "2nd place at IIT Roorkee (550+ teams)"
}; |
# AI helps explore architectures
# I validate with CS fundamentals
approach = {
"ideation": "Claude suggests patterns",
"validation": "I verify scalability/performance",
"implementation": "AI writes boilerplate",
"optimization": "I fine-tune for production",
"result": "87% latency reduction in Web Navigator"
} |
// Complex bug that takes others 2 days?
// I solve it in 2 hours.
debuggingFlow = {
step1: "LLM analyzes error patterns",
step2: "I apply algorithmic thinking",
step3: "AI suggests 5 potential fixes",
step4: "I choose optimal solution",
edge: "Minutes vs hours saved per bug"
}; |
// Traditional: Read docs → Build demo → Move on
// Me: Ship production project → Learn by debugging real issues
let learning_velocity = LearningApproach {
method: "Build real systems with AI assistance",
outcome: "Learn by solving actual problems",
speed: "10x faster than tutorials",
proof: "3 production systems in 6 months"
}; |
🎯 The Result: Built GHOSTCUT (forensic AI auditor) in 3 weeks. Traditional approach would've taken 8 weeks. Won 2nd place at IIT Roorkee.
+ 🎯 Built RAG + NLI system validating claims across 10,000+ chunks with evidence-backed outputs
+ ⚡ Improved retrieval quality by 38% using TF-IDF + SBERT hybrid ranking strategy
+ 🧠 Achieved 91% entailment accuracy with RoBERTa-based natural language inference
+ 📊 Developed Trust Score (0-100) + real-time React dashboard with <200ms latency
+ 🗄️ PostgreSQL backend handling concurrent queries at production scale
+ 🏆 Won 2nd place at IIT Roorkee E-Summit 2026, beating 550+ teams nationally
 RoBERTa NLI validation |
 Hybrid TF-IDF+SBERT |
 Real-time dashboard |
 Production scale |
🎯 Why It Matters: Solves misinformation by providing forensic-level claim verification. This isn't a demo—it's a production-grade AI system that companies would pay for.
✅ Selected for T-Hub Hyderabad (Top 1% Among 1000+ Participants Across India)
+ 🤖 Autonomous browser agent executing complex workflows across 100+ webpages per session
+ ⚡ Reduced execution time from 55 minutes → 7 minutes (87% faster) via Playwright concurrency
+ 📊 Processed 50,000+ records with 95% extraction accuracy using LLM + rule-based parsing
+ 🎯 Structured data extraction with zero-shot learning and adaptive CSS selectors
+ 🚀 Production-ready async pipeline handling thousands of concurrent browser sessions
+ 🏆 Selected for T-Hub Hyderabad incubation (Top 1% nationally)
 55min → 7min pipeline |
 Production scale |
 LLM + rule parsing |
 Autonomous workflow |
🎯 Why It Matters: 87% time savings = massive cost reduction for businesses. This isn't web scraping—it's intelligent browser automation at scale.
+ 🔍 Evaluated 1,000+ queries, detected 30-45% missing evidence in production RAG pipelines
+ 📈 Reduced irrelevant retrieval noise by 28% using coverage scoring + top-k analysis (k=5-20)
+ 📊 Generated explainable diagnostics dashboards, improving debugging speed by 60%
+ 🛠️ Production-ready evaluation framework for enterprise RAG systems
+ 🎯 Identifies retrieval gaps, hallucination risks, and context quality issues
 Production RAG audit |
 Coverage scoring |
 Explainable diagnostics |
🎯 Why It Matters: RAG is everywhere in 2026. Most implementations are broken. This audits and fixes them at enterprise scale.
|
API Development:
Database Mastery:
System Architecture:
|
RAG Pipelines:
NLP & Transformers:
Automation & Agents:
|
|
Microsoft Azure (Certified):
Infrastructure:
Monitoring:
|
Competitive Programming:
Core Expertise:
System Design:
|
💡 Philosophy: Master the fundamentals. Use AI to move faster. Ship production systems.
 Open source backend contributions |
 LeetCode + CodeChef (1400+ rating) |
 Real systems, not demos |
 Microsoft Learn Student Ambassador |
const sriRam = {
identity: "AI Systems Engineer, not just a developer",
core_beliefs: {
execution: "Shipping > Talking. Production > Portfolios. Impact > Hype.",
learning: "Build real systems. Fail fast. Learn faster. Repeat.",
ai_native: "AI is a force multiplier. Mastery = knowing when to use it.",
competition: "Compete with yesterday's self. Help others win too.",
fundamentals: "Deep CS knowledge + AI velocity = unstoppable"
},
mission_2026: [
"🎯 Land MAANG internship by shipping undeniable work",
"🚀 100x my life using AI as a competitive advantage",
"❤️ Make my farmer father proud by becoming world-class",
"🌍 Build AI systems that solve real problems at scale",
"🔥 Prove elite engineers come from anywhere in India"
],
what_drives_me: `
I don't compete with other developers.
I compete with AI-powered developers.
And I'm winning.
`,
approach: "Deep fundamentals + AI velocity = 10x output",
next_12_months: {
technical: "Master distributed systems, contribute to major OSS",
career: "MAANG internship Summer 2026 → SDE role",
impact: "Mentor 500+ students, ship 10 production AI systems",
growth: "Transform from student to industry-ready engineer"
},
current_status: "MAANG or bust. No Plan B. 100% execution mode.",
hiring_status: "Open for Summer 2026 internships. Ready to start immediately."
};
console.log("While others collect certifications, I collect production wins.");
🚀 Availability: Immediate for Summer 2026 internships
💻 Location: Open to remote, hybrid, or relocation
🎯 Focus: Backend systems, AI/ML engineering, full-stack AI products