Hi, I'm
Shahzaib Ali Senior Full-Stack & AI Engineer
I build production web products end-to-end: frontend, backend, data, infrastructure and AI, with React, Next.js, Node.js and AWS, plus hands-on RAG systems, LangGraph agents and tool-calling workflows.
7+ Years
20K Active users
700ms Down from 30s
50+ Engagements
Experience
My Journey at a Glance
From hands-on full-stack development into technical leadership, without stepping away from engineering.
System Nexgen
LahoreDec 2018 – Jun 2019Junior Full-Stack Developer
- Built ERP features with React on the front end and .NET MVC on the back end.
- Shipped inventory and reporting screens with their supporting APIs and queries.
- React
- jQuery
- .NET MVC
Technerds
LahoreJun 2019 – Feb 2020Full-Stack Developer
- Built React front ends and Node.js APIs for a healthcare platform used by patients, clinics and practitioners.
- Delivered the appointment scheduling module end to end.
- React
- Node.js
- Express
- Design to React Conversion
Freelance
RemoteFeb 2020 – Sep 2022Full-Stack Developer
- Completed 50+ React and Node.js engagements for international clients, owning scoping, architecture and delivery through post-launch support.
- Built 6 full-scale applications among them: e-commerce platforms, booking systems and admin dashboards.
- React
- Node.js
- Express
- MySQL
- MongoDB
- Client Communication
Ropstam Solutions
IslamabadSep 2022 – Aug 2026MERN Stack Developer
- Sole full-stack developer on a live real-time concert platform serving 20,000 active users, built on AWS Lambda, API Gateway and Aurora MySQL.
- Owned features end to end across UI, API and database.
- Cut a core report from 30 seconds to 700 milliseconds through query-level filtering, indexing and removing redundant fetches.
- Traced drifting like and comment counts to a read-modify-write in application code and replaced it with an atomic database increment.
Team Lead, MERN
- Final reviewer on every team pull request, and set the React and Node conventions the team worked to.
- Mentored 6 developers through daily delivery and code review; 3 progressed from junior to full-stack roles.
Engineering Lead
- Built a LangGraph-based prospect research and qualification agent for an outreach product, modelling multi-step targeting as a directed graph rather than a linear script.
- Combined JINA search and Cheerio-based web extraction with automated website auditing.
- Moved long-running campaign execution onto BullMQ background jobs, outside the request cycle.
- Owned front-end and back-end architecture across 4 concurrent client projects.
- Led requirement discovery with clients and produced the technical plans and estimates behind 6 project wins.
- Next.js
- Node.js
- MongoDB
- LangGraph
- BullMQ
- AWS
- Team Leadership
- Project Estimation
- Requirement Gathering
Capabilities
What I Do
Full-stack product engineering, with AI built into real applications rather than bolted on as a demo.
Full-Stack Product Engineering
Features built and shipped across frontend, backend, APIs, databases and infrastructure, owned from requirements through production.
- React
- Next.js
- TypeScript
- Node.js
- Express
- AWS
Retrieval Systems (RAG)
Retrieval that knows when its context is too thin. Intent routing skips retrieval that isn't needed; results are graded for relevance, weak queries are rewritten and re-issued, and the answer is "I don't know" rather than a confident guess.
- Chunking
- Embeddings
- Pinecone
- Retrieval scoring
- LangChain
Agentic Workflows
Multi-step work modelled as a directed graph rather than a linear script, with tool calling and long-running steps moved onto queues. Shipped in a production outreach product.
- LangGraph
- Tool calling
- OpenAI API
- Queue-backed execution
API and Data Architecture
APIs, schemas and data flows designed around real product requirements, with an emphasis on maintainability, performance and data integrity. Long-running work moved onto queues, outside the request cycle.
- REST API design
- MongoDB aggregation
- PostgreSQL
- MySQL
- BullMQ
Performance and Reliability
Production issues investigated at the system level, from inefficient queries and redundant data access to concurrency bugs and long-running workloads, then fixed at the cause. One core report went from 30 seconds to 700 milliseconds.
- Query profiling
- Indexing
- Concurrency
- AWS Lambda
- CloudWatch
Portfolio
Featured Work
Production-oriented work: the problem, the engineering decisions, and what made it hold up in real use.
Ask Shahzaib- An Agentic RAG Chat
A chat app that answers questions about me in my own voice, grounded in a private corpus of my CV and notes. A LangGraph agent decides whether a question needs the knowledge base, judges whether what it retrieved is good enough, and declines playfully rather than inventing an answer.
Blog
Latest Insights
Thoughts on software architecture, AI, and the craft of building products.
Retrieval is a similarity guess and a language model handed a bad guess answers anyway, fluently and with total confidence. Here is the six-node agent I used to make a chatbot about me decline instead of invent, and what that architecture actually costs per question.
Next Step
Senior Full-Stack & AI Roles
I'm open to Senior Full-Stack, AI Product Engineering and LLM application roles. I enjoy products where strong software engineering and practical AI need to come together.