Engineering journal · Business problems & practical delivery
How I Build and Optimize Full Stack AI Products
RIA Catalyst: building for deal research
I worked on RIA Catalyst as a full-stack engineer using Next.js and MongoDB. The product provides AI-powered M&A intelligence and deal sourcing for Registered Investment Advisors. My contribution connected the web application and its data layer within that business context.
The application supports a specific commercial activity: researching and discovering deal opportunities. My role was building the product that supports that work. This gives recruiters and clients a concrete example of my full-stack AI experience in a domain where the interface and stored information need to serve a research workflow.
Vital Edge Fit and Study Mind: making AI part of the application
On Vital Edge Fit, I worked with Next.js and Supabase on a healthcare and fitness application with secure authentication, patient records and chatbot appointment booking. On Study Mind, I worked with Next.js and LLM integration on document-based reasoning, notes and study scheduling.
These projects show how I handle AI features as part of a wider application. Vital Edge Fit connected conversational functionality with appointment workflows. Study Mind connected document interaction with learning tools. My contribution spanned full-stack implementation rather than treating the AI response as a standalone deliverable.
Subscription infrastructure: supporting the product after signup
At Cybernef Technologies, I designed and maintained Stripe subscription infrastructure covering plans, the billing portal, invoices, webhooks and trial or usage enforcement. I also worked on pytest coverage for Stripe webhooks, engine routing and RAG retrieval pipelines.
This work supported the operational side of subscription products: billing behavior, customer account workflows and usage rules. It also brought testing into the integration layer. My full-stack responsibilities included the infrastructure needed to operate the application alongside its user-facing and AI capabilities.
Optimization across retrieval, data and delivery
I designed a RAG system with Pinecone and LangChain to improve semantic information retrieval. In my Summit Crew work, I optimized SQL and NoSQL operations to reduce query latency. At Shyft Digitally, debugging and tests for React components and REST APIs were part of my application performance work.
I also configured monitoring and observability and managed CI/CD on AWS. These are concrete parts of how I optimize products: improve retrieval, reduce database delays, test application behavior and make releases repeatable. My experience connects those improvements across the frontend, backend and AI portions of the system.
I’m Abaid Ullah, a Full Stack AI Software Engineer open to opportunities in this area.
I build AI-enabled web products that connect frontend experiences, backend services and business data. My work spans React and Next.js interfaces, Python and Node.js backends, databases and practical AI integrations.
Skills relevant to this role
React, Next.js, TypeScript, Python, Node.js, MongoDB and Supabase.
Role interests: Full-stack product development, AI-enabled SaaS and web application engineering.
Connecting the product layers
Selected project experience
RIA Catalyst
AI-powered M&A intelligence and deal-sourcing platform built for Registered Investment Advisors (RIA dealmakers).
My role: Full-stack (Next.js + MongoDB)
Vital Edge Fit
Healthcare & fitness SaaS featuring secure auth, patient records, and an integrated chatbot for automated appointment booking.
My role: Full-stack (Next.js + Supabase)
Study Mind
Interactive learning platform with a NotebookLLM-style framework for document-based reasoning, notes, and study scheduling.
My role: Full-stack (Next.js + LLM)
Discuss a Full Stack AI Engineer opportunity
I have worked with international teams across the USA, Canada, Europe and Asia, including the UAE. I’m available for global teams and remote collaboration.
Send the role description, product goals and technical requirements so we can discuss the relevant experience.
My tools for this work
RAG specialist and knowledge architecture
RAG, GraphRAG, Knowledge graph RAG, Microsoft GraphRAG, LlamaIndex, Haystack, Qdrant, Pinecone, pgvector, Neo4j, LangChain, ColPali, Unstructured, Hybrid search, Reranking.
Agent architecture and orchestration
LangGraph, CrewAI, AutoGen, PydanticAI, GPT-Researcher, browser-use, OpenHands, Mem0, Model Context Protocol (MCP), Custom MCP servers, FastMCP, Text-to-SQL agents.
Backend systems, data and distributed work
Python, FastAPI, Django, Flask, Node.js, NestJS, Express, PostgreSQL, MySQL, MongoDB, Supabase, Redis, DynamoDB, Queues, Background workers, REST APIs.
Web, mobile and product delivery
React, Next.js, React Native, TypeScript, JavaScript, Tailwind CSS, Redux, SQLite, Recharts, NFC, NTAG 424 DNA, OAuth, MFA, Spec-driven development, Solution architecture, Technical product delivery.