Backend Focus
Scalable APIs, services, and distributed workflows.
I build practical products with scalable backend systems, clean APIs, distributed workflows, and AI-driven automation across agents, RAG pipelines, and full-stack experiences.
C++ | Backend Engineering | Distributed Systems | AI Agents | RAG | APIs | Docker
1700+
LeetCode
contest rating
370+
Problems
DSA problems solved
900+
Contributions
open-source activity
30+
Projects
public builds
System architecture
Backend + AI product workflow
Client
web app
API Gateway
routing
Services
logic
Data Layer
cache + db
AI Agent
RAG tools
Low-latency APIs
Clean services
RAG orchestration
Docker-ready
Recruiter Snapshot
A quick scan of the signals that matter: backend depth, AI product execution, DSA strength, product ownership, and deployment readiness.
AI Product Experience
RAG, LLM apps, AI agents, and automation.
DSA Strength
370+ LeetCode problems and 1700+ rating.
Product Builder
Real projects with practical use cases.
Deployment Mindset
Docker, APIs, and cloud-ready systems.
Selected Case Studies
Deep dives into backend systems, AI-driven products, and engineering decisions behind my projects.
AI System
Multi-Modal RAG
Shows applied AI engineering beyond notebooks: retrieval quality, modular services, and deployable system design.
Problem: Knowledge retrieval across documents, web, audio, and video becomes fragmented without a unified ingestion and retrieval layer.
Solution: Built a RAG pipeline that turns heterogeneous inputs into searchable context for grounded responses.
Architecture: Ingestion workers, embedding generation, vector search, retrieval ranking, LLM response orchestration, and Dockerized deployment.
PythonLangChainGeminiVector DBDocker
AI System
ShopSense
Demonstrates product thinking, API integration, and AI-backed recommendation workflow.
Problem: Product discovery needs comparison, Q&A, and purchase intent in one flow.
Solution: Built an AI shopping assistant that compares Amazon products, answers questions, and sends selected product links by email.
Architecture: Product ingestion, assistant workflow, comparison layer, email delivery, and full-stack user interface.
Next.jsTypeScriptMongoDBAPIsEmail
Full Stack
InstaLens
Combines data extraction, AI analysis, and usable product UI for decision support.
Problem: Instagram profile analysis is time-consuming for influencer and brand decisions.
Solution: Built a full-stack analytics platform with scraping, AI content intelligence, and dashboard reporting.
Architecture: Scraping layer, AI analysis services, dashboard views, profile metrics, and content intelligence modules.
ReactNode.jsAI APIsAnalyticsDashboards
Full Stack
SmartVault
Signals scalable full-stack engineering and clean product architecture.
Problem: Secure file workflows need clean auth, metadata, and reliable backend design.
Solution: Built a vault-style web application with structured storage, metadata handling, and user-focused flows.
Architecture: Auth-aware frontend, backend APIs, database models, file metadata, and deployment-ready project structure.
TypeScriptBackend APIsMongoDBAuthCloud
AI System
Research Paper RAG
Shows practical LLM orchestration for research, education, and enterprise knowledge workflows.
Problem: Research paper Q&A needs accurate context retrieval from academic documents and web sources.
Solution: Implemented a RAG pipeline using LLMs with vector search for context-aware answers.
Architecture: Document parsing, chunking, embeddings, Qdrant/Chroma retrieval, Gemini response generation, and source-grounded Q&A.
PythonGeminiQdrantChromaRAG
Backend Engineering Focus
A system-design inspired view of how I think about APIs, services, data flow, reliability, and deployment.
API Design
Service Layer
Database
Cache
Queue
Observability
Deployment
API & Service Design
REST APIs, request validation, clean service boundaries, and modular backend structure.
Scalability
Caching, async processing, queue-based workflows, and distributed design patterns.
Data Layer
PostgreSQL, Redis, vector databases, schema design, and retrieval systems.
Reliability
Error handling, logging, retries, rate limits, and production-oriented thinking.
AI-Driven Products & Agents
Applied AI work across agents, retrieval, tool calls, backend orchestration, and structured product workflows.
User Input
Agent Reasoning
Tool/API Calls
Retrieval
Backend Service
Final Response
AI Agents
Systems that can reason, call tools, use APIs, and automate workflows.
RAG Pipelines
Document ingestion, chunking, embeddings, vector search, and LLM-generated answers.
LLM Applications
Practical AI products using LLM APIs, backend orchestration, and structured outputs.
Automation
AI workflows for search, summarization, compliance, health reminders, and document intelligence.
Skills
Grouped by how they are used in real builds: backend systems, AI workflows, programming, core CS, and delivery tools.
Backend
FastAPINode.jsREST APIsPostgreSQLRedisDocker
AI Systems
RAGLLM APIsEmbeddingsVector DatabasesAI AgentsLangChain
Programming
C++PythonJavaScriptTypeScriptSQL
Computer Science
DSAOOPDBMSOSCNSystem Design Basics
Tools
GitGitHubDockerAWSVercelLinux
Resume
A concise overview of my education, projects, skills, and engineering experience.
Himanshu Raj
Founder | Builder | Strong Problem Solver
B.Tech CSE-DS
Projects: AI QA, RAG, Backend Systems, Distributed Workflows
Focus: reliable products, scalable services, and intelligent automation
Let's build reliable systems and intelligent products.
I am open to backend engineering, software engineering, AI product engineering, and early-stage startup opportunities.