Parth ModiAhmedabad, India/8+ years in production
Building scalable SaaS products, cloud systems, and high-performance web applications.
Node.js
Used extensively for scalable REST APIs, background processing and SaaS backend services.
Real figures from production work
Parth Modi
Senior Full Stack Developer · Technical Lead · Ahmedabad, India
012017 · DeveloperFull-stack development
My first production work was backend logic for three live Android and iOS apps. Since then I have moved through every layer: React and Next.js on the screen, Node.js and Express behind it, and the database underneath.
Full-stack development
022018 · Senior DeveloperQuality at traffic
High-traffic service portals in a six-person Agile team taught me cross-browser discipline, code that QA stops sending back, and picking the simplest tool that does the job.
Quality at traffic
032019 → · Team LeadTeam leadership & architecture
I lead four engineers: planning, code review, mentoring, and the architecture decisions about boundaries, data, and what should happen asynchronously.
Team leadership & architecture
04Now · Product EngineerSaaS, scalability & AWS
SEOSpace went from about 100 users to 40,000+ across 120+ countries. That meant queues, caching, and AWS services like S3, CloudFront, Lambda, and SQS, and thinking about the product a customer actually uses.
SaaS, scalability & AWS
Three companies, one steady climb: from writing the APIs to deciding how the whole system fits together.
Webcore Solutions
Ahmedabad, India
Key achievement
Backends for three live mobile products.
Techno Adviser Technologies Pvt. Ltd
Ahmedabad, India
Key achievement
Consistent cross-browser front end that cut bugs reported by QA.
Qodic Technosoft LLP
CurrentAhmedabad, India
Key achievement
Scaled SEOSpace from about 100 to 40,000+ users across 120+ countries, against a 99% uptime target.
Three SaaS products in market and the browser extensions behind them. Each card opens a case study with the problem, the approach, and what I was responsible for.
01SEO plugin & toolkit for Squarespace
SEOSpace
An SEO SaaS that helps Squarespace sites get found, from Google rankings to how they show up in AI answers. It runs as a web app and inside the Squarespace editor as a Chrome extension.
Up from about 100 users
02Text recruiting software
Keeyora
Candidate texting for talent teams. Recruiters message from their ATS or a Chrome extension, with templates, automations, and SmartDraft AI drafts a person reviews before sending.
03AI marketing agents for self-storage
StorIQ
AI agents built only for self-storage operators. They manage Google Business Profile, paid search, and SEO continuously, with rank tracking and reporting tied to move-ins.
04Software inside pages I do not own
Chrome Extensions
Production Chrome extensions, including enterprise extensions migrated from Manifest V2 to V3. Background pages became service workers, UI loads only when it is opened, and every part talks through explicit messages.
I've shipped production Chrome extensions and migrated enterprise extensions from Manifest V2 to V3. Background pages became service workers, UI loads only when it's opened, and every part talks through explicit messages. Here's how a single request moves through one.
❯ chrome.runtime.sendMessage({ type: 'ANALYZE_PAGE' })
The user acts in the extension panel. It sends a typed message to the service worker.
Message flow
What this covers
Pick any technology to see what I use it for, where I've used it, and what it connects to.
AI accelerates my workflow.
I still own the engineering decisions.
I use AI as an engineering tool, the same way I use a debugger or a profiler. It shortens the distance between a question and a careful change. The review, the tests, and the production behaviour are still mine.
01
I define what is actually broken or needed, and what done looks like.
I own this
02
Read unfamiliar code, compare approaches, and draft options faster.
AI-assisted
03
I check the options against the system, the data, and the constraints.
I own this
04
I write and review the change. AI helps with refactors and boilerplate.
Me + AI
05
Generated tests are a starting point. I keep the ones worth keeping.
Me + AI
06
I own the deploy, the monitoring, and what happens after.
I own this
AI in the products
Products I've worked on that put AI in front of users.
SEOSpace
AI visibility: how a site shows up in ChatGPT, Gemini, and AI answers.
Keeyora
SmartDraft AI writes recruiter messages that a person reviews before sending.
StorIQ
AI marketing agents that manage local search and ads for storage operators.
Leading isn't only my own tickets. It's the plan, the review, the decision, and the production system after the decision.
4.
Engineers on my team
Planning. Break work down, sequence it, and keep Jira honest about what is actually next.
The same loop on every project, from a small fix to a new service. Select a stage.
Understand.
Start with the user and the constraint, not the framework. What is the real problem, and how will we know it is solved?
One engineer who can take a product from the data model to the deploy, and lead the team that ships it.
Data model, API, UI, and the AWS deploy behind it. One person who can follow a bug through every layer.
Node.js services, queues, and caching that took SEOSpace from about 100 to 40,000+ users.
React, Next.js, and TypeScript interfaces that stay clear as features pile up.
S3, CloudFront, Lambda, SQS, API Gateway, and EventBridge in production.
Hot paths in Redis, heavy work off the request path, and mobile screens loading in under two seconds.
Calm when the live system is the one that is broken, with a 99% uptime target to hold.
Leading four engineers through planning, reviews, mentoring, and architecture calls.
Talks with clients and stakeholders in product terms, not only in tickets.