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aadeshmahesh/README.md

Hi, I'm Mahesh 👋

Senior Software Engineer | Frontend Engineering & Agentic AI Applications
16 years experience · Scottsdale, AZ · Open to opportunities

I bring 16+ years of software engineering experience, building enterprise web applications and leading frontend delivery. My core strengths are React, TypeScript, component architecture, API integration, and engineering mentorship.

I also build personal AI applications with Node.js and the Anthropic API, exploring how retrieval, tool calling, and human approval can support useful application workflows.


What I'm Building

I'm going deep on agentic AI engineering — not just prompting, but building agents from scratch. Tool use, RAG pipelines, MCP servers, human-in-the-loop patterns, and multi-agent orchestration.


AI Agent Projects

Project What it does New concept
Job Application Agent Analyzes job descriptions against resume using pgvector semantic search — returns match score, skill gaps, and learning recommendations. Auto-saves to application tracker. Live on Railway + Cloudflare Resume as RAG KB + skill gap analysis + production deploy
Document Intelligence Pipeline Upload PDF → S3 → Lambda → Bedrock Claude → DynamoDB → Q&A AWS serverless + Docker + CI/CD
AEM Knowledge Agent Internal KB agent — answers AEM questions, finds page owners, troubleshoots errors Hybrid search (SQL + pgvector) + Redis cache
Code Review Agent Multi-agent code review — Security, Performance and Style agents run in parallel Multi-agent orchestrator pattern
HR Onboarding Agent Onboards employees — pauses for manager approval, resumes via webhook Human-in-the-loop + persistent state
Restaurant Agent Food recommendations with all production patterns MCP + streaming SSE + Redis cache
Restaurant MCP Server Standalone MCP server — any AI connects via SSE URL MCP protocol + Zod validation
Site Search Agent Semantic product search — finds "running shoes" from "footwear for jogging" Real embeddings + pgvector
IT Support Agent Diagnoses and fixes VPN + tool access RAG + parallel tools
Calendar Agent Schedules meetings from natural language Agentic loop + tool use

AI Concepts I've Built

✅ Agentic Loop          while(stop_reason === "tool_use")
✅ Tool Use              LLM decides, your code executes
✅ Parallel Tool Calls   Multiple tools in one LLM turn
✅ RAG (keyword)         JSON search before acting — Project 2
✅ RAG (semantic)        Voyage AI embeddings + pgvector — Project 3
✅ Cosine Similarity     Vector distance for ranking
✅ MCP Server            Tools exposed via standard protocol
✅ MCP Client            Agent connects to MCP via SSE URL
✅ Streaming SSE         Tokens appear live in React UI
✅ Redis Caching         Repeated searches served instantly
✅ Rate Limiting         Bottleneck for Anthropic API
✅ Zod Validation        Safe tool inputs — no LLM hallucination
✅ Human-in-the-loop     Agent pauses → webhook → resumes
✅ Persistent State      Full session saved to Neon DB
✅ Idempotent Tools      Safe to retry — never duplicates work
✅ State Machine         started → waiting_approval → completed
✅ Session Logging       Full request/response + token cost
✅ Multi-Agent           Orchestrator + 3 parallel specialists
✅ Promise.all()         All specialist agents run simultaneously
✅ Hybrid Search         SQL keyword + pgvector semantic combined
✅ Configurable Search   Runtime switch between search modes
✅ SQL KB Pattern        LLM queries structured DB via tools
✅ AWS Lambda            Serverless event-driven functions
✅ S3 Event Triggers     Upload → auto-trigger Lambda
✅ Docker + ECR          Containerized Lambda functions
✅ CI/CD                 GitHub Actions → ECR → Lambda deploy
✅ Resume as RAG KB      Claude splits resume → Voyage AI embeds → pgvector search
✅ Skill Gap Analysis    Semantic JD vs resume comparison with match scoring
✅ Application Tracking  Status machine: saved → applied → interviewing → offer
✅ Production Deploy     Railway (Node.js) + Cloudflare Pages (React)
✅ JWT + Rate Limiting   Auth + express-rate-limit protecting Anthropic API costs

Stack

AI
Anthropic Claude · Voyage AI · MCP Protocol · RAG · pgvector · Streaming SSE · AWS Bedrock · AWS Textract

Frontend
React 18/19 · TypeScript · Redux Toolkit · TanStack Query · Vite · AWS Amplify

Backend
Node.js · Hono · Express · Cloudflare Workers · AWS Lambda · Zod · Bottleneck

Data
Neon Postgres · Drizzle ORM · Upstash Redis · pgvector · AWS DynamoDB · AWS S3

DevOps
Docker · AWS ECR · GitHub Actions · AWS API Gateway · AWS CDK · Railway · Cloudflare Pages


16 Years of Engineering

2010 → PHP, Perl, jQuery — e-commerce foundations
2015 → React, Redux — modern frontend
2020 → TypeScript, micro-frontends, design systems
2026 → AI agents, RAG, MCP, AWS serverless, agentic engineering

Key work: Migrated 150+ retail sites (Bazaarvoice → PowerReviews) and LivePerson migration. Oracle Endeca search integration, Adyen payments, Perl automation eliminating manual regional rollouts across large e-commerce program.


Portfolio

maheshbuilds.dev — case studies, blog, and AI engineering notes

Pinned Loading

  1. calendar-agent calendar-agent Public

    AI agent that schedules calendar events using natural language — agentic loop, tool use, Ollama + Anthropic Claude

    JavaScript 1

  2. it-support-agent it-support-agent Public

    AI agent that diagnoses and fixes IT issues using RAG + parallel tool use — searches company docs before acting, then checks and restores VPN and internal tool access. Node.js + React.

    JavaScript 1