Najeeb Ahmad K.

Most "AI engineers" on here can wire up an OpenAI API call. Few can take a system that's wrong 3 times out of 4 and make it reliable enough to ship to real users. I took a production RAG agent from 27% to 79% accuracy on a real evaluation set, not by swapping models, but by fixing retrieval and orchestration. That's the work most people skip because it's harder than it looks and less fun to talk about. I'm Najeeb, CTO of BrainGenz, an AI development team based in Lahore. 13 years building production systems, the last several focused on LLM/RAG applications that have to work under real constraints: HIPAA compliance, PHI redaction, real-time fraud detection on imbalanced data, terabyte-scale pipelines. What I actually build: RAG & Agent Systems: hybrid retrieval (BM25 + vector + reranking), multi-agent orchestration, eval harnesses that tell you when something breaks, not just when it "feels off" Full-Stack AI Products: Next.js/TypeScript frontends, FastAPI/Python backends, Postgres, multi-tenant architecture, Stripe billing baked in from day one MLOps & Deployment: Docker, AWS/GCP/Azure, CI/CD, systems that survive contact with production traffic Rather than take my word for any of this, I have a live multi-tenant AI platform running in production right now, agents, billing, tenant isolation, the whole stack. It's linked in my portfolio section below if you want to see it firsthand. Background: production RAG systems at Elevance Health under HIPAA, terabyte/day Spark pipelines at Cognizant, fraud/credit risk models (XGBoost/LightGBM) at Citi. AWS ML, GCP Data Engineer, and Azure Data Engineer certified. If your project needs someone who'll tell you what's actually hard about it before quoting a number, let's talk.