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Anurag Singh

Software Engineer → AI Engineer

I build AI , Products and Systems that ships.

LLMs · RAG · Full Stack · Production Systems

4+ years shipping scalable software. Now turning that craft into intelligent products people can trust.

Anurag Singh, software engineer transitioning into AI and ML

Experience

Impact over activity.

Professional experience is the core of this portfolio. Each role is framed by outcomes — systems shipped, workflows improved, and ownership demonstrated.

  1. Build and ship ecommerce platform systems — APIs, admin tools, and AI-assisted workflows that keep catalog, inventory, and orders running at scale.

    Tenure

    3+ years

    Domain

    Ecommerce Ecosystem software

    Focus

    Full Stack + AI

    Key responsibilities

    • Ship full-stack features in React/Next.js and Node.js for catalog, inventory, and order systems used daily in production.
    • Design APIs and data models for complex ecommerce rules; keep services fast, maintainable, and easy to extend.
    • Build AI-assisted tools for catalog enrichment, search relevance, and ops automation using LLMs and embeddings.
    • Partner with stakeholders to turn bottlenecks into shipped product — clearer workflows, fewer handoffs.

    Achievements

    • Owned 20+ production features live in US ecommerce operations.
    • Reduced multi-step admin workflows from 8+ clicks to 2–3 with unified tooling.
    • Introduced LLM/RAG helpers that cut catalog and support triage time by ~30%.

    Technologies

    ReactNext.jsNode.jsExpressMongoDBTypeScriptOpenAILangChainRAGVector DBAWSREST APIs

AI Journey

From products to intelligence.

4 years shipping software — now building AI into everything I make.

  1. 01

    Full Stack

    Shipped end-to-end products — UI, APIs, data, deploy.

  2. 02

    Backend

    APIs, data models, and systems that scale with the business.

  3. 03

    Automation

    Cut manual ops work with reliable software workflows.

  4. 04

    LLMs

    Built product features powered by language models.

  5. 05

    RAG

    Grounded AI answers in real product and domain data.

  6. 06

    ML

    Models, evaluation, and data-first problem solving.

  7. 07

    AI Engineer

    Shipping production AI products people can trust.

Skills

A stack shaped for production AI.

Grouped by capability — not progress bars. Depth in software engineering, expanding into modern AI systems.

Languages

Core languages for systems, products, and AI work.

PythonJavaScriptTypeScriptC++

Databases

Persistent storage and caching for production workloads.

MongoDBPostgresRedisSQL DBVector DB

Backend

APIs and services designed for scale and maintainability.

Node.jsExpressFastAPI

Frontend

Product interfaces with clarity, speed, and polish.

ReactNext.jsVueAngularSvelteRemixTailwind

AI & ML

The stack powering the transition into AI engineering.

PythonNumPyPandasMatplotlibScikit-learnPyTorchTensorFlowLangChainLlamaIndexRAGVector DatabasesEmbeddingsPrompt EngineeringOpenAI APIsMCPAI Agents

Deployment

Shipping and operating reliable production systems.

DockerAWSGitHub ActionsCI/CD

Engineering Impact

4+

Years Experience

Shipping production software across startups and product teams

10+

Production Applications Built

From healthcare PWAs to commerce and AI-assisted products

Large-scale

APIs & Workflows

Backend services supporting complex operational business logic

Millions

Records Processed

Catalog, order, and operational data handled in live systems

5+

AI-Oriented Projects

Chatbots, LLM UX, and AI-assisted product experiments

Multiple

Automations Built

Workflow automation that reduced manual operational overhead

25+

Technologies Used

Across frontend, backend, cloud, and emerging AI tooling

Real users

Users Impacted

Live products serving customers, patients, and internal operators

Featured AI & Product Work

Selected systems, not a project dump.

A curated set of products that show engineering maturity, product thinking, and an emerging AI-first craft.

Tourplanner AI preview
LLM Application

Case study

Tourplanner AI

Conversational trip planning powered by AI assistance

Problem

Travelers struggle to assemble itineraries across destinations, budgets, and preferences — often bouncing between fragmented tools with no intelligent guidance.

Solution

Built a full-stack tour planning product with chatbot-assisted itinerary generation, enabling users to explore and refine travel plans through natural conversation.

Architecture

Next.js frontend with conversational UI, Node.js services for planning workflows, and chatbot integration for guided itinerary creation. Deployed as a production web application.

Business value

Turns trip planning from a manual research chore into an assisted product experience — reducing decision friction and demonstrating practical LLM UX in a consumer domain.

Next.jsReactNode.jsChatbotAI UXVercel
GitHubDemo
Ayum.in preview
Production Platform

Case study

Ayum.in

Healthcare PWA from zero to production

Problem

Healthcare access workflows needed a reliable digital product that patients and providers could use without native app friction — with production-grade uptime and maintainability.

Solution

Designed and engineered a progressive web application covering core healthcare flows, shipping a complete stack from UI to database and deployment.

Architecture

Next.js + React client as a PWA, Node.js backend services, MongoDB persistence, and Vercel-hosted delivery with production deployment ownership.

Business value

Enabled a live healthcare product (ayum.in) with end-to-end engineering ownership — proving ability to take ambiguous product needs to production systems.

Next.jsReactNode.jsMongoDBPWAVercel
GitHubDemo
Thoughtshare preview
Full Stack System

Case study

Thoughtshare

Notes sharing platform with full CRUD workflows

Problem

Students and professionals needed a simple, reliable way to publish, organize, and share important notes without heavyweight tooling.

Solution

Engineered a full-stack notes platform with authentication-ready sharing flows and complete create, read, update, and delete operations.

Architecture

React frontend consuming Node.js APIs with MongoDB for document storage — modular CRUD services and responsive UI for browsing and managing notes.

Business value

Delivered a complete content-sharing product loop — demonstrating scalable CRUD architecture, data modeling, and user-facing reliability.

ReactNode.jsMongoDBExpressREST
GitHubDemo
MachZ Clothing preview
Commerce Platform

Case study

MachZ Clothing

E-commerce storefront with realtime-backed commerce flows

Problem

Independent apparel brands need a polished storefront with catalog browsing and cart flows without standing up heavy custom infrastructure.

Solution

Built a full-stack e-commerce experience on React and Firebase, covering product discovery and purchase-oriented user journeys.

Architecture

React client with Firebase-backed data and auth primitives — modular product, cart, and checkout-oriented UI layers for a responsive commerce experience.

Business value

Showcases product thinking in commerce: conversion-oriented UX, clean component architecture, and practical use of managed backend services.

ReactFirebaseE-commerceJavaScript
GitHubDemo
Analytics Dashboard preview
Data Interface

Case study

Analytics Dashboard

Trading-style analytics UI for dense operational data

Problem

Dense operational and trading metrics are hard to scan when interfaces lack hierarchy, visual clarity, and responsive chart composition.

Solution

Designed a responsive analytics dashboard with graph-driven views and a disciplined visual system for monitoring key signals.

Architecture

React UI with composed chart modules, theming, and layout systems optimized for readability across desktop and mobile viewports.

Business value

Demonstrates interface engineering for data-heavy products — the same craft required for ML observability, ops consoles, and AI evaluation dashboards.

ReactChartsDashboard UIResponsive Design
GitHubDemo

Engineering Philosophy

How I think about building.

A concise operating system for solving problems, learning in public through projects, and shipping software that lasts.

01

How I solve problems

I start from the business constraint, not the framework. Clarify the failure mode, define the smallest reliable interface, then iterate with measurable outcomes.

02

How I learn

I learn by building. Foundations first — systems, data, and evaluation — then apply them in projects that resemble production constraints.

03

How I build scalable systems

Clear boundaries, boring reliability, and extensible design. Prefer maintainable architecture over clever abstractions that only the author understands.

04

Why AI excites me

AI is leverage on knowledge work. The opportunity is not demos — it is reliable systems that retrieve, reason, and act with software-grade accountability.

05

Production software mindset

Ship what operators can trust: observability, graceful failure, clean contracts, and code that the next engineer can extend without archaeology.

Contact

Let's Build Something Intelligent.

Open to AI engineering roles, LLM application work, and teams building production systems with serious craft.

Email meView Resume
EmailAnuragsinghg999@gmail.com
LinkedInProfessional profile
GitHubCode & experiments
ResumePDF download