I'm Muhammad Irfan — I build agentic LLM pipelines, RAG, and the evaluation & guardrails that make AI trustworthy enough to ship. 13+ years engineering; the last few focused entirely on production AI, under my practice SmartOps.
Most teams can get an LLM to look like it works. The hard part — the part I'm hired for — is making it dependable in production. Four ways I help:
Multi-agent orchestration with structured outputs, validation gates, a failure taxonomy, and guardrails — taking pipelines from plausible drafts to a ~90% successful-run rate.
Retrieval-augmented systems that return grounded answers, not confident hallucinations — backed by a golden-task eval set built from your real questions, so quality is measured.
Turn slow, manual, repetitive work into reliable AI workflows wired into your existing tools and systems — outcomes, not experiments.
Wire LLMs into your product and backend — Python, APIs, AWS/Azure — engineered for production, security, and cost from day one.
Senior, lean, and measurable — here's how an engagement runs.
We start where it's costing you — a focused look at where your AI or pipeline breaks today, and what "good enough to ship" actually means for your use case.
Structured outputs, guardrails, retrieval, and clean integration into your existing stack — engineered for production, security, and cost, not just a demo.
An evaluation harness so every improvement is provable, plus documentation and a clean handover so your team can own and extend it.
Recent client work is largely proprietary, so these are genericized case studies — happy to walk through the real architecture and trade-offs on a call.
Designed a planner → implementer → reviewer pipeline that turns requirements into production-grade code, tests & docs — with structured outputs, validation gates, a failure taxonomy, and an evaluation harness so quality is measured, not guessed.
Treated RAG quality as an evaluation problem: ground answers in retrieval, then build a golden-task eval set from real user questions plus scoring rubrics and regression checks — so every change is measured against your data, and drift is caught before users see it.
Added the reliability layer to a flaky LLM feature — structured outputs, validators, fallbacks, and a failure taxonomy catching the points where multi-step reasoning breaks (loops, bad tool calls, context blowups) — and made every change measurable via evals.
Irfan did an excellent job… excellent at understanding requirements and getting work done with efficiency and accuracy. Keen to use again.
I'm Muhammad Irfan, an applied-AI / LLM engineer with 13+ years shipping production software. I currently build LLM orchestration and agentic systems at Chatari; before that I led engineering in regulated healthcare (HIPAA / ISO 27001 / ADHICS) in Abu Dhabi, and shipped consumer apps to millions of users (8M+ downloads, a #1 App Store utility, a Macworld Best of Show).
SmartOps is my practice. I work senior, lean, and honest — I lead by building alongside you and owning the outcome, and I'll tell you plainly if something's a stretch.
I'll tell you honestly whether I can help, and how I'd start — usually with the failure that's costing you the most, made measurable. Free 20-minute call, no pitch.