🧠 I am an enterprise-level full-stack and forward-deployed engineer who bridges deep technical execution, from web and mobile to custom AI architecture, with strategic business problem-solving.
I build AI-enabled systems for Healthcare Providers, Legal Support Firms, Construction firms, Real Estate Firms, Operations-heavy Businesses, and B2B SaaS teams where workflows are complex, regulated, and have to hold up under real production load.
Not AI demos. Not chatbots bolted onto existing software. Systems where AI is one carefully designed layer inside a reliable, auditable process that actually runs the business.
What I Help You Do:
Design AI-enabled platforms where the AI is reliable enough to put in front of patients, lawyers, or paying customers
Ship HIPAA-grade AI systems where PHI never leaks to an LLM, audit logs are append-only at the database level, and BAAs are signed across the full sub-processor chain
Build legaltech operations platforms with AI document parsing, multi-tenant isolation enforced at the database (PostgreSQL Row Level Security), and AI that never modifies the legal record
Automate document workflows, intake, triage, dispatch, and internal operations with AI where it has an edge and deterministic code where it doesn’t
Develop AI-enabled SaaS products from MVP to production-ready platforms with senior architectural judgment, not just code delivery
Extend AI capability into mobile apps, web platforms, and frontend/backend systems end to end
Wire AI into no-code and low-code workflows using n8n, Make, GoHighLevel, and Zapier when those tools are the right fit
Fix and stabilize existing AI systems that are slow, hallucinating, or failing compliance reviews
How I Build AI Systems That Survive Production:
Most AI projects fail because they focus on the model instead of the workflow around it.
I design systems where AI is one layer inside a reliable process:
Ahmad K. earns an estimated $5.9k/mo. That's 4.1× the typical freelancer and more than 99.63% of everyone we track.
PHI and sensitive data de-identified before anything reaches an LLM, provable from API logs
Human-in-the-loop on every AI output that touches a regulated record
AI used for unstructured-to-structured work; deterministic code for calculation and rule enforcement
Append-only audit logs enforced at the database permission level, not the application
Tenant isolation at the database, not the application
Clear fallback, error handling, and observability from day one
Every AI call logged, costed, and traceable
This is what separates an AI system that ships from one that survives a HIPAA review, a court challenge, or a security questionnaire from a Fortune 500 procurement team.
What You Get:
AI architecture that holds up under regulatory scrutiny and senior technical review
Systems that turn hours of manual work into minutes without losing control
Clean code your team can maintain, with documentation a senior engineer can read in one sitting
Senior-level judgment on where AI fits, where it doesn’t, and what to build vs. buy
Real business impact (time saved, workflows automated, compliance posture strengthened)
Core Stack for AI-Enabled Systems:
AI and orchestration: OpenAI, Claude, RAG pipelines, LangGraph, vector databases (Pinecone, pgvector)
Backend: Python (FastAPI, Django), Node.js
Frontend: Next.js, React, TypeScript, React Native (mobile)
Data: PostgreSQL with Row Level Security and pgcrypto, Redis, APIs, integrations (Stripe, Twilio, CRM systems, EHRs via FHIR R4)
No-code and low-code automation: n8n, Make, GoHighLevel, Zapier
Infrastructure: AWS, GCP, Docker, Vercel, all with BAA-covered configurations for healthcare workloads
Compliance fluency: HIPAA Security Rule (45 CFR §164.308, §164.310, §164.312), HITECH breach notification, FHIR R4, SMART on FHIR, BAA chain management, court-grade evidence handling for legaltech
If you’re trying to put AI inside a real business and you need it to work in production, in regulated environments, at the standard a senior engineering team would actually trust, I can help you design and ship it