Building production AI systems, agentic workflows, automation, full-stack products, and reliable cloud infrastructure.
Building & shipping software since 2020
I'm Waseem Akram, an AI Engineer and Full-Stack Software Engineer from Pakistan.
I've been building software since 2020, starting with Linux, scripting, automation, cybersecurity, and web development, then expanding into full-stack product engineering, mobile apps, browser extensions, developer tooling, cloud infrastructure, and AI systems.
Today, my main focus is AI engineering, agentic systems, and AI automation — building systems where LLMs, agents, tools, APIs, retrieval, structured data, automation workflows, and traditional software architecture work together in production.
I don't treat AI as a replacement for engineering.
I use AI as part of the engineering stack while still owning:
- Architecture
- Backend & APIs
- Databases
- Frontend & mobile
- Infrastructure
- Security
- Testing & debugging
- Evaluation
- Production delivery
AI can generate code. Engineering is deciding what should be built, how it should work, whether it is correct, and whether it deserves to reach production.
|
Chrome Users
Across published extensions |
VS Code Installs
Developer tooling |
GPT Conversations
Custom ChatGPT GPT |
GitHub Followers
Developer community |
3 Android apps published · Multiple 4.6★+ products · Largely organic growth
These are intentionally shown as separate metrics — users, installs, conversations, and community reach — rather than combined into an artificial "total users" number.
I build AI systems as part of the application architecture, not as thin wrappers around a chat box.
User / Application
│
▼
AI Orchestration
│
┌──────┼────────────┐
▼ ▼ ▼
LLM Tools Retrieval
│ │ │
└──────┼────────────┘
▼
APIs / Services
│
▼
PostgreSQL / Data
│
▼
Infrastructure
AI Agents · Agent Orchestration · AI Automation · LLM APIs · Tool Calling · RAG · Structured Outputs · Context Engineering · Local LLMs · Evaluation
I actively use AI coding agents for:
- Planning
- Implementation
- Refactoring
- Debugging
- Testing
- Code review
- Research
- Documentation
But architecture, constraints, validation, and production decisions remain engineering responsibilities.
I've shipped multiple production Android applications through Google Play.
A privacy-focused learning platform built with React Native/Expo and backed by a full-stack API and data platform.
A published Android application available on Google Play.
A production habit-tracking application published on Google Play.
flowchart LR
A[Problem] --> B[Architecture]
B --> C[Plan]
C --> D[Agent Delegation]
D --> E[Review]
E --> F[Test]
F --> G[Security]
G --> H[Ship]
H --> I[Observe]
I --> A
My workflow is built around AI orchestration, not blind generation.
Understand the problem
↓
Design architecture
↓
Break work into executable tasks
↓
Delegate appropriate work to AI agents
↓
Review implementation
↓
Test assumptions and edge cases
↓
Debug / refactor
↓
Validate security and data flows
↓
Ship
↓
Observe real-world behavior
↓
Iterate
The goal is not to generate more code.
The goal is to ship better software faster without giving up engineering judgment.
Cybersecurity is where a major part of my engineering journey started, and that background still shapes how I think about authentication, permissions, API design, browser security, data exposure, and defensive engineering.
I'm currently going deeper into:
Agentic AI
LLM application architecture
AI orchestration
RAG & retrieval systems
AI evaluation
Tool / function calling
Context engineering
Local & open-source LLMs
Full-stack AI products
React Native
Next.js
PostgreSQL / Supabase
Cloud infrastructure
CI/CD & automation
Production AI reliability
I'm particularly interested in roles such as:
- AI Engineer
- AI Automation Engineer
- Full-Stack AI Engineer
- Agentic AI Engineer
- AI Product Engineer
- Software Engineer — AI Platform
- DevOps / Cloud Engineer for AI-enabled products
I've been building software since 2020.
Cybersecurity & Linux
↓
Automation & Scripting
↓
Web Development
↓
Browser Extensions
↓
Full-Stack Engineering
↓
Mobile Development
↓
Cloud & DevOps
↓
AI Engineering & Agentic Systems
That breadth is intentional.
Understanding systems from the browser and application layer down to APIs, databases, infrastructure, Linux, networking, and security makes me a stronger AI engineer today.
Build → Ship → Measure → Learn → Improve
I care about practical engineering over hype.
A beautiful architecture nobody uses is not automatically better than a boring system serving thousands of people reliably.
Likewise, an application is not valuable simply because it contains an LLM.
It should solve a real problem.
- Active Directory Pentesting Resources (2025 Guide)
- Cipher's Secret Message – TryHackMe Walkthrough
- Volatility Essentials Walkthrough – Complete Memory Forensics Guide with Volatility 3
- Roundcube RCE Vulnerability Explained [CVE-2025-49113]
- Getting Started with Bash Scripting
