Senior AI EngineerIslamabad, Pakistan (UTC+5)Open to consulting

Muhammad Tayyab:I get LLM systems out of the prototype and into production, inside software that already exists.

Seven years shipping production software, now building multi-agent systems, AI evaluation pipelines and LLM integrations with legacy platforms at a leading Southeast Asian personal-finance platform.

Hiring? LinkedIn for hiring teams (opens in a new tab)Or send a short note

Featured explainer71s · captions

Watch the explainer: Autonomous Content System, 71 seconds

How a multi-agent system drafts, safety-gates and publishes unattended, behind a three-layer fail-closed judge. Read the case study

Outcomes at a glance

01 / In production

A multi-agent system live in production about three months after the first commit.

Autonomous Content SystemCase study : Autonomous Content System

02 / Platform

A governed workflow-automation platform now hosting dozens of production automations.

AI Paved RoadsCase study : AI Paved Roads

03 / Reliability

A prototype turned into a stable daily tool, every failure mode pinned by a test.

AI Design PlatformCase study : AI Design Platform

01 / Selected work

AI systems in production, and the engineering decisions behind them.

Thirteen case studies. Each one has a narrated explainer of about a minute, with captions and a full transcript.

Production systems at a consumer fintech

2022 to 2026

Earlier engineering work · 2020 to 2023

Show 6 projectsHide

My current employer's name is kept confidential.

02 / How I can help

Most AI work fails at integration, not intelligence. That is the part I work on.

01

Adding LLMs to software that already exists.

Wiring models into legacy platforms is my day job: the Claude SDK inside Rails systems, async job layers for work that outgrows a request timeout, and API contracts other teams can safely build on.

  • LLMs in legacy codebases
  • Custom AI integrations
  • Rails and microservices

See: Claims Ops Platform

02

Agents you can trust to act unattended.

Multi-agent systems where code owns the critical path and models work at the leaves, with fail-closed judges, dry-run and canary modes, kill switches and idempotent side effects.

  • Multi-agent systems
  • LLM-powered applications

See: Autonomous Content System

03

Knowing whether the AI is getting better or worse.

Evaluation pipelines and LLM-as-judge gates, tracing through Langfuse, and tests that assert real behavior rather than surface indicators.

  • AI evaluation pipelines
  • LLM observability
  • System design

See: Autonomous Content System (three-layer LLM judge)

04

A team that needs to get fluent with AI.

I lead LLM adoption across departments, and I have trained 6,000+ participants in applied AI through a joint AISkillBridge and HEC programme, plus a 100-student agentic AI cohort.

  • LLM adoption and strategy
  • Applied and agentic AI training
  • Technical leadership

See: Teaching and background

How to start

Start with an intro call about what you are building and what is already in production.

Remote from Islamabad, Pakistan (UTC+5). Fluent in English, Urdu and Punjabi.

Book an intro call (opens in a new tab)

03 / Endorsements

What clients and employers say.

His ability to quickly understand complex problems and deliver optimal solutions is truly extraordinary. We have successfully developed numerous web and mobile applications together, and his work always exceeds expectations.

Ahmed DarwishCEO at OK TAMAM

The experience with Muhammad was exemplary. From quality of code to transparency, communication and fluently speaking English. I highly recommend Muhammad. Give him a chance to prove his skills and you won't be disappointed.

Marius PopClient via Upwork · Upwork profile (opens in a new tab)

04 / About

An engineer first. That is what makes the AI part actually work.

Portrait of Muhammad Tayyab
Islamabad, Pakistan

Seven years shipping production software: Islamic-banking software on Oracle Flexcube, Rails APIs, leading a team of Flutter developers, then Rails microservices in consumer fintech, before I moved into AI. Today I build multi-agent LLM applications in production as a Senior AI Engineer.

That background is the point: wiring an LLM into a legacy codebase and keeping it reliable is engineering, not prompting.

  1. Since Jan 2026Available for AI consultingIndependent · RemoteOpen to enterprise work: LLM adoption and strategy, multi-agent system design, integrating AI into production systems.
  2. Aug 2022 to nowSenior AI EngineerConfidential · Consumer FintechRails Developer to Senior Software Engineer to Senior AI Engineer. Multi-agent LLM systems in production; introduced Kafka for inter-service messaging.
  3. Mar 2021 to Jul 2022Technical LeadOK TAMAM Group · IstanbulLed five Flutter developers. Admissions platform used by 40+ universities and agencies.
  4. Jul 2020 to Mar 2021Software EngineerDevsinc · LahoreRails APIs and a React frontend for a US real estate platform.
  5. Jul 2019 to Jul 2020Software EngineerTechlogix · LahoreProfit distribution for Islamic banking on Oracle Flexcube. Deep SQL work.

Teaching

  • 6,000+ participantsLLM fundamentals, prompt engineering and everyday AI tooling, through a joint AISkillBridge and HEC programme.
  • 100-student agentic AI cohortAI Trainer at AISkillBridge, volunteer, since Jul 2025.

Education

  • MS Computer Engineering (AI)Bahçeşehir University, 2021 to 2023
  • BS Computer ScienceFAST-NUCES, 2015 to 2019. Silver Medalist.

Awards

  • Winner, AI Seekho Builders Day 2026NIC Islamabad, for Nigehbaan
  • ACM-ICPC Runner-Up, 2019Winner, PASTIC-UAF ICT Expo, 2019

05 / Contact

Got an LLM stuck at the prototype stage? Let's talk about it.

Book an intro call, or write a few lines about what you are building, what is already in production and what is blocking you.

Goes straight to my inbox at tyb9900@gmail.com.