Forward Deployed Engineer · Enterprise AI · Architecture to cloud
I build AI-native enterprise platforms from scratch and bring AI into the systems businesses already run. I own the full path, from architecture and code to secure cloud deployment, with privacy, access control and human oversight built in.
Enterprise platforms for HR, learning, public-sector and healthcare clients. AI inside CRMs, calling agents and a live marketplace. AI products with real users.
Certified by Anthropic, Amazon Web Services, IBM, DeepLearning.AI and PIAIC
Operator.profile
Online
Muhammad Abdullah
Forward Deployed Engineer
Current run · lead-outreach
Example agent run: contacts searched and outreach drafted. Waiting for a human to approve before anything is sent.
For recruiters
Shipped with teams at
Aization
Enterprise AI · NDA
CS2 Technologies
Toronto · Canada
Call Agent AI
Canada
01 / Selected work
Showing 11 projects
Person
A manager describes a training need
AI
AI plans and writes the course
System
Automatic quality check
Output
Course, narrated videos and quiz
An enterprise learning platform where AI does the heavy lifting: it writes courses, produces narrated training videos and grades written answers, while managers stay in control and costs stay within limits.
Learning and corporate training · NDA
An AI layer for a public employment program: it builds job-seeker profiles from CVs, tailors resumes without inventing anything, and matches people to jobs with scores anyone can understand and audit.
Public sector employment · NDA
An adaptive assessment that measures employees' AI skills, grades written answers with AI under strict safeguards, and gives everyone a personal learning path, while people review anything uncertain.
Enterprise workforce and HR · NDA
A hiring assessment platform where recruiters create tests by describing the role, candidates take them securely, and every score comes from proven, auditable scoring methods rather than AI opinion.
HR technology · NDA
A live platform that turns a person's Canadian immigration profile into a personalised AI strategy report. Accurate calculations provide the facts, AI plans the strategy, and the report arrives live and as a PDF.
Immigration
A CRM that teams operate straight from the AI assistant they already use. The AI prepares emails and updates, and a person approves anything that is sent, deleted or charged.
Sales and CRM
A spreadsheet-driven consulting practice turned into a secure, multi-client platform: separate workspaces for every client, one-click remediation plans, ongoing monitoring and an AI assistant that never mixes clients' data.
Healthcare cybersecurity and compliance · NDA
Instead of writing a separate integration for every CRM, I built one universal connector that lets an AI calling agent work with many CRMs, and delivered a full Jobber integration end to end.
AI voice and customer service
AI added to a live wholesale marketplace: a 24/7 AI sales agent, deals sent to the CRM in one click with their activity shown back, and AI that drafts personalised outreach for a person to approve.
B2B wholesale commerce
An AI system that reads 56 Reddit communities for small business owners with real problems, drafts a helpful reply in the founder's voice, and sends it to Slack for a person to approve.
AI voice and customer service · NDA
A full HR and payroll platform with an AI HR assistant, taken from a blank start to a secure, cloud-hosted product the client could test in about eleven days, through three changes of cloud plan.
HR technology · NDA
02 / Experience
Clients in the UAE · Remote
AI Engineer
I design and build enterprise AI platforms for clients in HR technology, learning and the public sector, bringing software engineering and system design to AI work, from architecture to production on Azure. Client work is under NDA.
AI-Native Corporate Learning Platform Explainable AI Talent-Matching Platform for the Public Sector Adaptive AI-Readiness Assessment Platform AI Talent Assessment Platform HR and Payroll Platform
2026 – Now
Toronto, Canada · Remote
Agentic AI Engineer (contract)
I build AI products for the company's own portfolio: an AI-native CRM run through MCP, an AI immigration strategy platform, and AI sales tooling on a live B2B wholesale marketplace.
Jan 2026 – Now
Canada · Remote
AI Engineer (contract)
I build AI integrations and automations for an AI calling company: a universal CRM connector that lets its AI agent work with HubSpot, Zoho, Jobber and more, and an AI-assisted community engagement pipeline with review in Slack.
Universal CRM Connector for an AI Calling Agent AI Lead Discovery on Reddit, With a Person Approving Every Reply
2026 – Now
UK client · Remote
Lead full-stack engineer
Turned a healthcare compliance firm's spreadsheet practice into a multi-tenant platform with an AI assistant, and deployed it to the client's own server.
From Spreadsheets to a Healthcare Compliance Platform with AI
2026
PIAIC · Bano Qabil
Agentic AI and Python
Designed and taught courses on agentic AI, Python and FastAPI for agents.
Ongoing
03 / What I do
/build
Multi-tenant SaaS where AI is part of the production line: content generation, RAG, agents, voice and grading, built on an architecture that can be operated and audited.
/integrate
Add AI sales agents, assistants, CRM integrations and human-in-the-loop workflows to live platforms, without rewriting what works or risking production data.
Including MCP server development.
/ship
Cloud infrastructure on AWS and Azure written as code, containers on Docker and Kubernetes, automated deployments with no stored passwords, secure secrets handling, load testing and clear handover documentation. I don't hand over a demo; I hand over a running system and the guides to operate it.
The trust layer, across all three
How I work · local to production
Step 01
Audit the requirements
I check the brief against reality in week one and log every decision the client makes.
Step 02
Design the boundaries
What code decides, what the model does, where the human signs off, and where the data lives.
Step 03
Build with proof
Tests, CI gates and live click-through verification from the first sprint.
Step 04
Ship and hand over
Cloud infrastructure written as code, a live deployment, and documentation the client's team can run with.
04 / Writing
AI Engineering · 11 min
Will AI replace software engineers? My answer from a real enterprise project: AI sped up the work, but human judgment cut a 29-second wait to half a second.
Nov 7, 2026
About
I'm Muhammad Abdullah, a forward deployed engineer for enterprise AI, based in Pakistan and working remotely with clients and teams in Canada, the UAE, the UK and beyond.
My work falls into three kinds of problem. I build enterprise platforms with AI at the core, like a learning platform that writes its own courses and a talent platform that explains every match it makes. I connect AI to the systems a business already runs, like one connector that lets a calling agent work inside HubSpot, Zoho and Jobber. And I ship AI products people use every day, like a CRM you run from ChatGPT or Claude.
The industry has changed from project to project: HR, learning, healthcare compliance, sales, immigration and voice AI. The hard parts haven't. Messy data, strict access rules, AI output that has to be checked, and systems that can't go down. So a new domain doesn't start from zero: I learn the business and its data first, then apply patterns I've already shipped.
I started as a software engineer, and it shows in how I build AI: client data protected in the database itself, decisions made by rules that can be tested, AI with spending limits and a person who can overrule it, and systems I take to the cloud myself.
Much of my client work is under NDA, so the case studies describe the problems and my engineering decisions, never the clients.
Education
BS Computer Science
University of Sahiwal · graduating 2027
4.0 / 4.0 CGPA
Credentials & teaching
Certifications
Verified training from Anthropic, Amazon Web Services, IBM, DeepLearning.AI and PIAIC. The case studies show how I put it to work on real systems.
Anthropic
Advanced MCP patterns for connecting AI assistants to real business systems, the approach behind AgentlyLeads, the CRM you can run from ChatGPT and Claude.
Anthropic
The open standard that lets AI models use tools and data safely, and the foundation of the MCP servers I build.
Presidential Initiative for Artificial Intelligence & Computing (PIAIC)
Building production MCP servers that give AI agents secure, controlled access to business tools.
Certificate no. 2026040195591
See it in practicePresidential Initiative for Artificial Intelligence & Computing (PIAIC)
Designing AI agents that plan, use tools and hand decisions to a person where it matters.
Certificate no. 2026010195591
DeepLearning.AI · via Coursera
Grounding AI answers in an organisation's own documents, so responses stay accurate, current and on topic.
Amazon Web Services · via Coursera
AWS cloud foundations behind the secure AWS environments I deliver for clients.
IBM · via Coursera
Designing applications as independent services and serverless functions that are easier to scale and maintain.
IBM · via Coursera
Packaging applications into containers with Docker, which I use to deploy systems like the HR and payroll platform and the Reddit lead discovery pipeline.
FAQ
Yes. I've added an AI sales agent, a CRM integration and human-approved outreach workflows to a live B2B marketplace, and an AI governance assistant to a running compliance platform. I work behind feature flags, fail softly at every integration boundary and test against production schemas before anything ships.
Yes. I've built multi-client AI platforms from scratch and delivered them to enterprise clients, owning the design, backend, frontend, AI, cloud infrastructure and automated deployments.
Tenant isolation is enforced in the database with row-level security, PII is redacted before anything reaches a model, audit logs are append-only, and I deploy into the client's cloud and region. I've shipped a platform that builds and deploys inside an air-gapped environment.
Code decides and the model explains. Scores, rankings and eligibility come from deterministic, tested code. The model drafts, grades against a rubric or writes the narrative, and its output is validated, has a fallback and can be overridden by a person.
Not for NDA work. I describe the problem, the architecture and my decisions, and I'm happy to walk through the design on a call.
Pakistan (UTC+5), remote-first. I'm one hour ahead of the UAE, and my hours are flexible, so I overlap with teams in Canada, the US, the UK and Europe too.
Maybe not yet, and that is rarely the hard part. I've shipped for HR technology, learning, the public sector, healthcare compliance, B2B sales, immigration and voice AI. What carries over is the engineering: keeping each customer's data apart, connecting to the tools you already use, checking AI output before it reaches anyone, and running it all reliably in the cloud. I spend the first days learning your domain and your data.
05 / Contact
UTC+5 · flexible hours · one hour from the UAE, easy overlap with Canada, the US, the UK and Europe