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Open to new work — full-time or contract.
I'm a full-stack engineer with 6+ years of experience, working with TypeScript across the stack. Most of that time has been spent embedded in a small number of products for years at a time, rather than rotating between short gigs — which means I've owned things long enough to live with my own decisions.
What I build:
AI/LLM products — tool-calling agent workflows that act on results autonomously, retrieval-grounded generation over large corpora, structured outputs with validation, and evaluation against real outcomes. Built in-house, with the model provider behind a service boundary so routing stays swappable.
Payments — Stripe Connect and Subscriptions in production: recurring billing, plan changes, usage-based charges, marketplace payouts, and webhook-driven reconciliation so the ledger stays accurate when events arrive late, twice, or out of order.
Integrations — 15+ third-party APIs including Amazon Seller/Vendor Central, Google, and Microsoft Graph behind one normalized interface, cutting integration time by ~40%.
Real-time & scale — WebSocket services maintaining consistent state for thousands of concurrent users, with reconnection and out-of-order delivery handled explicitly.
Stack: TypeScript · React · Next.js · Node/Express · PostgreSQL · Redis · AWS · Docker · CI/CD. Claude Code and MCP tooling are part of my daily workflow.
If you're shipping something early-stage and need someone who can take a feature from schema to production without a handoff, my DMs are open.
good luck in your job search Ali
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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My core stack is less "generate pretty pictures" and more "build the pipeline that does the thinking":
n8n — the glue. Great for orchestrating multi-step agent workflows and connecting APIs without writing a service from scratch. Weakness: gets messy fast on complex branching logic, and debugging a broken node chain eats time.
RAG (usually with a vector DB + LangChain-style retrieval) — lets me ground LLM output in actual client data instead of hallucinated generalities. Strength: dramatically improves accuracy for domain-specific asks. Weakness: chunking/retrieval quality is finicky, and a bad embedding setup silently tanks results without throwing an error.
Claude/GPT for reasoning + code — my default for anything needing judgment: analysis writeups, SQL/Python generation, proposal drafting. Strength: fast iteration. Weakness: still needs a human check on numbers — confident-sounding ≠ correct.
Power BI / Tableau — where the AI output actually becomes something a client trusts. AI can shortcut the analysis, but the dashboard is what gets signed off on.
The honest lesson: the "AI" part is maybe 30% of the workflow. The rest is validation, data plumbing, and making the output legible to a non-technical stakeholder.
Silent failures are what I watch closest. I treat an answer as untrusted unless I can show exactly which source text the model read, because fluent output can hide a bad match.
A multi-tenant AI orchestration platform that lets organizations deploy and manage AI agents at scale. It supports persona-based agents with defined roles and behaviors, webhook-driven triggers for automated actions, and single-prompt management of complex multi-step workflows. Built to run reliably on Kubernetes and Docker across multiple organizations.
Whats your thoughts on this?
Very impressive architecture. Multi-tenancy, persona-based agents, and Kubernetes scalability make this a strong foundation for enterprise AI automation. 🚀