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.

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

Build things. Ship them. Let real users decide.

AI Engineering · AI Automation · Agentic Systems · Full-Stack · DevOps · Cloud · Security