Kent · Suhail Shah
I'm Kent (Suhail Shah), an applied AI engineer specializing in LLM pipelines. I build retrieval, agent orchestration, evals, and the backends that keep them reliable in production.
An AI coding agent built on multi-provider orchestration with retries and fallback, AST-aware code operations, and per-call cost tracking.
Production systems designed and built inside companies, with a focus on applied AI.
Senior Applied AI Engineer
An internal AI knowledge and drafting platform built for insurance claims operations. The system enabled claims staff to retrieve policy information, understand procedures, locate product details, and draft internal communications through a citation-grounded retrieval architecture combining hybrid search, reranking, structured lookups, and evaluation-driven development.
Senior Applied AI Engineer
Joined as the first engineer focused on AI products and led the development of a multi-tenant knowledge platform that transformed internal documentation into a citation-grounded conversational search experience. Over twelve months the platform evolved from a simple vector-search prototype into a production knowledge system featuring hybrid retrieval, reranking, integrations, evaluation frameworks, observability, analytics, and knowledge-health tooling.
Senior Software Engineer
A document-intelligence product taken from early stage to production, on a two-service architecture: a Node and TypeScript product layer alongside a Python OCR and NLP extraction service, joined by a typed contract.
Senior Software Engineer
A travel-and-expense workflow system rebuilt and shipped to production. The core design is a composable architecture where workflow templates and form templates are independent reusable building blocks configured as data, so new request types ship as configuration rather than code.
Software Engineer
The backend API and web operations dashboard for an outdoor-events security operation, with API contracts coordinated with a separate mobile team. Replaced spreadsheet scheduling and paper attendance with a constraint-based scheduling engine, QR check-in with GPS verification, and a real-time operations dashboard.
A complete look at the architecture, engineering decisions, and capabilities behind the product.
Four repositories, three deployment targets, one cohesive product.
The key technical achievements. Click any card to expand.
Multi-stage pipeline that structures, validates, and verifies every AI response before touching code.
AST-powered code understanding that reads only what's needed and writes with surgical precision.
Full auth system with JWT, OAuth, CLI device-code flow, and per-user project isolation.
Stripe-powered credit system with per-call metering and real-time balance enforcement.
React-based terminal UI published on npm with live activity feed, model switching, and session receipts.
Production-hardened with rate limiting, input validation, error sanitization, and monitoring.
Personal AI systems work, built in public with readable source.
Public source, actively developed
A local learning platform where an LLM acts as the teacher and a typed Python backend owns memory, orchestration, and context engineering. The public source spans the applied AI stack: provider abstraction over two model transports, pgvector retrieval, a versioned eval framework with regression reporting, OpenTelemetry tracing, and an approval-gated agent layer.
Hey, I'm Kent. I build the layer between raw model capability and software people can actually trust.
Here's the bet I'm making with my career. Every powerful tool has always charged an entry fee. Blender, Photoshop, a serious spreadsheet, each one takes months before it gives anything back. AI collapses that fee into a conversation. You say what you want and the tool meets you there. Wrapping hard software in plain language is the biggest shift in how people use computers since the GUI, and it gets won or lost at the application layer. That's where I work.
A model on its own is an engine on a stand. Loud, impressive, going nowhere. I build the car around it: retrieval and orchestration as the drivetrain, evals as the brakes, observability as the dashboard. I work the whole machine because the interesting failures hide between the parts. A retrieval bug can look like a prompt problem, and a cost spike can really be a chunking decision. Raw capability is getting cheap. Proof that it works is the hard part, and that's the part I love.
The name “Kent” is a personal nickname derived from the meaning behind my given name, Suhail. In Arabic, Suhail is associated with Canopus, one of the brightest stars in the night sky and a historic navigational star known for guidance and direction. Inspired by that connection, I adopted Kent as the name I use professionally and in everyday life.
Whether you know me as Suhail or Kent, they're both part of the same story—and both refer to the same person.
Interested in working together? Let's connect.
Open to full-time Applied AI Engineer roles · Remote