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A1man1/Readme.md

Aman Developer Banner

Software Engineer | 5+ Years Experience
RAG Data Platforms | Semantic Search Optimization
LLM Indexing & Retrieval Pipelines
Fine-Tuning (PEFT, LoRA, QLoRA)
Kafka Processing 2M+ Events/Day

💡 Use dark mode for the intended experience.

🔴 🟡 🟢   > whoami

aman@dev-box ~ % whoami --verbose

OS: Backend & Data Engineer Shell: Python (Django, FastAPI) · Java (Spring Boot) Experience: 5+ years

Specialization: RAG Data Platforms Semantic Search Optimization LLM Indexing & Retrieval

Fine-Tuning: PEFT · LoRA · QLoRA Throughput: Kafka pipelines, 2M+ events/day Scale: Backend platforms serving 10K+ users

Optimizations: API latency -35% Search query time -83%

Currently: Exploring deep neural nets & LLM internals LeetCode: 300+ problems solved

⚡ > Philosophy

Complex systems don't fail because they are complicated — they fail because they aren't understood.

The most interesting engineering problems appear when systems begin to scale.

That's the space I enjoy working in.

THINK BUILD SCALE REPEAT

♙   > About Me

Backend & Data Engineer with 5+ years of experience building scalable systems, high-performance data platforms, and — more recently — LLM-powered retrieval infrastructure.

I turn distributed systems, event pipelines, and backend architectures into platforms that stay fast, stable, and resilient in production.

🚀 Mission

Scalable design patterns, done right.

I build with Python, Java, SQL & NoSQL systems, cloud platforms, and modern UI frameworks — with data management and distributed-systems architecture at the core.

📊 Delivering Impact

• Event-driven pipelines processing 2M+ events/day
• Backend platforms serving 10K+ users
• API latency reduced by 35%+
• Search query times cut by 83%

🧠 AI / LLM Systems Work

• RAG data platforms & retrieval pipelines
• Semantic search optimization
• LLM indexing & retrieval architecture
• Fine-tuning: PEFT, LoRA, QLoRA
• Data-intelligence layers feeding model workflows

📚 Exploration & Learnings

• 300+ LeetCode problems
• Deep-diving neural networks
• Exploring LLM internals
🎯 Looking ahead: distributed systems, cloud-native architecture, and the evolving stack connecting data platforms to intelligent, retrieval-augmented applications.

⚙️   > Tech Stack

🌐 Frontend

HTML5 · CSS3 · Angular · React · TypeScript · JavaScript

⚙️ Backend & Frameworks

Python · Django · FastAPI · Java · Spring Boot

🧠 AI / LLM & Data

RAG · Semantic Search · Embeddings · Retrieval · PEFT · LoRA · QLoRA · Kafka

🗄️ Databases

MongoDB · PostgreSQL · MySQL · SQL Server · Redis · Elasticsearch

☁️ Cloud

AWS · Azure · Google Cloud · Databricks · Lakehouse

🧰 Tools & Utilities

VS Code · Postman · Docker · Git · Linux · REST · CI/CD

📊   > GitHub Stats

GitHub Streak
⚙️ BACKEND
Distributed Systems
Microservices
System Design		 📡 DATA
Kafka
Data Pipelines
Lakehouse		 🧠 AI
RAG
LLM Retrieval
Fine-Tuning		 🔍 SEARCH
Semantic Search
Embeddings
Indexing

🔴 🟡 🟢   aman@dev-box — zsh

aman@dev-box ~ % echo "while (age++ < life.length): ++knowledge; ++experience; --ego; debug(failures)"

BUILD → SCALE → LEARN → REPEAT
IDEAS → SYSTEMS → IMPACT

Developer Footer

⚡ Building better systems, one failure at a time.

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