Full Stack Engineer · Bangalore, India
I'm a Full Stack Engineer building production web applications end-to-end — React and Next.js on the frontend, FastAPI, Go, and Node on the backend, deployed on AWS with Docker. I work across AI-integrated platforms, large-scale web crawling infrastructure, legal technology, design systems, and developer tooling.
Currently working at Perssonify and Virallens (sister companies) as a Full Stack Engineer. I came into web development from a Mechatronics Engineering background during COVID, and I care about the polish — fast builds, clean architecture, and interfaces that feel right.
A heads up if you're here to evaluate my work: what you see publicly is a small fraction of what I've built. I have 200+ private repositories that aren't visible on this profile.
The reason is simple — I've been burned before. People clone the code, strip the credits, and pass the work off as their own. Some have even used it in their own interviews. So most of my real work, especially client projects and original product code, stays private.
If you're seriously considering me for a role, I'm happy to walk you through any of it on a call — share my screen, explain architecture decisions, show commits, talk through trade-offs. Reach out via LinkedIn or email through my portfolio, and I'll set time aside.
A high-throughput crawling platform originally built in Python/FastAPI and now fully migrated to Go for performance. Microservices architecture with Docker Compose, orchestrated using Temporal, queued through Celery and Redis (early Python phase), with MongoDB and PostgreSQL for storage, deployed on AWS EC2.
The Python version got us to production fast and let us validate the architecture; the Go rewrite cut resource usage drastically, simplified concurrency, and made the crawl workers significantly more efficient at scale. Built a polished dark-themed dashboard UI with custom components, plus a Next.js marketing site. During the Python phase I optimized container memory from 8GB+ down to ~2.6GB through Dockerfile multi-stage caching with BuildKit, and reduced 8,000+ second build times. Set up multi-branch deployment strategy with GitHub Actions CI/CD.
A legal technology system crawling 180+ uscourts.gov domains for a securities litigation law firm. Originally built in Python/FastAPI with MongoDB and later migrated to Go for the heavy crawl and processing workloads. Role-based JWT authentication and a React dashboard for case analysis, with a stock drop analyzer featuring historical runs, re-analysis workflows, and tabbed report navigation.
The platform runs AI models in production for document classification, structured extraction from court filings, and signal detection — including Gemma running in production for self-hosted inference, alongside Claude and OpenAI APIs for higher-reasoning tasks.
Manifest V3 Chrome Extension built with Vite + TypeScript for a securities litigation law firm. Monitors a built-in database of ~20,000 stock tickers in real time, detecting them on financial sites and matching them against the firm's active case database. Uses AI models to analyze surrounding context — distinguishing meaningful signals from incidental ticker mentions — and surfaces relevant cases instantly to users. Backend in Express/MongoDB with OAuth, JWT auth, and proper API key security.
Shipped Next.js sites, component libraries, and full design systems across multiple verticals — Ellavox.ai (AI communication platform with cinematic dark/nature aesthetic, Framer Motion animations, bento grids), StarRise ABA Therapy (location-based landing pages), School Discovery, White Bridge Education (animated hero sections, world maps, team carousels), Kiwi Kids ABA Therapy, and DSS Fabrics (luxury dark/gold e-commerce).
Personal component library concept — 150+ animated React/Tailwind/Framer Motion components.
A growing chunk of what I do involves integrating LLMs into real products, not just prototypes:
- Gemma running in production — self-hosted inference for cost-efficient classification and extraction at scale.
- Claude API integrations for production legal-tech and content workflows — case analysis, document review, structured extraction.
- OpenAI API for content generation, summarization, and classification pipelines.
- Federal court AI monitoring — automated extraction and classification across 180+ government domains.
- Real-time AI signal detection — context-aware ticker analysis across 20k+ entities in the Chrome Extension.
- Tech news aggregation — automated pipelines for crawling, deduplicating, and summarizing.
- n8n workflow automation for orchestrating multi-step AI agents.
- Local LLM experimentation — agent setups integrating with Microsoft Teams and scheduling assistants.
I think a lot about prompt design, structured outputs, evaluation, and the boring-but-important parts of making AI features actually reliable in production.
Frontend — React, Next.js, TypeScript, JavaScript, Tailwind CSS, Framer Motion, GSAP, D3, shadcn/ui, Radix UI, Material UI, Zustand, Redux, Vue, Vite
Backend — Go, FastAPI, Python, Node.js, Express, NestJS, tRPC, GraphQL, REST APIs
Databases — PostgreSQL, MongoDB, MongoDB Atlas, Redis, Supabase, Prisma ORM, Firebase
Infra & DevOps — AWS (EC2, S3, ECR), Docker, Docker Compose, BuildKit, GitHub Actions, Nginx, Temporal, Celery, Vercel
AI Stack — Gemma (self-hosted), Claude API (Anthropic), OpenAI API, n8n, prompt engineering, structured outputs, agent workflows
Auth — JWT, OAuth, Clerk, Kinde, Auth0, Firebase Auth
Languages — Go, TypeScript, JavaScript, Python, C++, C#
Tooling — Git, Figma, Jira, Postman, Webpack, Vite, Chrome DevTools
Open to conversations about full-stack roles, AI-integrated products, and developer tooling work.
- Portfolio: syedmoinuddin.com
- LinkedIn: syed-moinuddin106
- GitHub: @SyedMoin-Lab