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Abdullah Z.

@redspector

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5.0
10

5.6

5.6

100%

Senior Developer | AI, Cloud, Full-Stack, ML

$25 USD / Hour

・

Flag of

Pakistan (7:26 PM)

・

Joined on July 2, 2025

$25 USD / Hour

・

I transform complex business challenges into scalable, intelligent solutions. I'm a full-stack architect specializing in AI-driven systems, cloud infrastructure, and secure DevOps practices. With 5+ years building high-performance applications, I help startups and enterprises scale reliably while reducing operational overhead. What I deliver: • AI & machine learning systems (Python, TensorFlow, transformers) • Scalable cloud architecture (AWS, containerization, microservices) • Secure DevOps pipelines (CI/CD, Linux, infrastructure-as-code) • Full-stack development (React, Node.js, .NET, React Native) • Enterprise security & compliance (OSCP, CEH certified) Recent wins: • Architected distributed systems reducing deployment time by 40% • Built ML pipelines improving prediction accuracy to 95%+ • Secured infrastructure achieving 99.9% uptime I'm detail-oriented, security-conscious, and committed to writing clean, maintainable code. Let's discuss how I can help your project succeed. Certifications: OSCP | CEH | AWS | Docker Languages: Python | Node.js | React | .NET

Verifications

On time

100%

On budget

100%

Accept rate

100%

Repeat hire rate

25%

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Portfolio

Portfolio

13842836
The Problem: Standard CPU computations for complex thermal and flux algorithms often bottleneck system performance. Simulating large-scale grids sequentially takes massive amounts of time and memory, making real-time analysis impossible for researchers and data-heavy enterprises. The Solution: I engineered a highly optimized, parallelized architecture to solve complex thermal dynamics. Instead of relying on a single-thread baseline, I architected a distributed-memory solver utilizing MPI (Message Passing Interface) across multiple nodes. To maximize single-node efficiency, I implemented thread-level parallelism using OpenMP. Finally, the core mathematical flux kernels were accelerated using GPU computing. This multi-layered approach (MPI + OpenMP + GPU) drastically reduced execution time, eliminated memory leaks, and ensured 100% hardware utilization. Key Deliverables Included: Standard C++ Baseline formulation. Distributed-memory solver (mpi_dist_solver.cpp). Shared-memory multi

High-Performance Computing: Thermal

The Problem: Standard CPU computations for complex thermal and flux algorithms often bottleneck system performance. Simulating large-scale grids sequentially takes massive amounts of time and memory, making real-time analysis impossible for researchers and data-heavy enterprises. The Solution: I engineered a highly optimized, parallelized architecture to solve complex thermal dynamics. Instead of relying on a single-thread baseline, I architected a distributed-memory solver utilizing MPI (Message Passing Interface) across multiple nodes. To maximize single-node efficiency, I implemented thread-level parallelism using OpenMP. Finally, the core mathematical flux kernels were accelerated using GPU computing. This multi-layered approach (MPI + OpenMP + GPU) drastically reduced execution time, eliminated memory leaks, and ensured 100% hardware utilization. Key Deliverables Included: Standard C++ Baseline formulation. Distributed-memory solver (mpi_dist_solver.cpp). Shared-memory multi

High-Performance Computing: Thermal

The Problem: Standard CPU computations for complex thermal and flux algorithms often bottleneck system performance. Simulating large-scale grids sequentially takes massive amounts of time and memory, making real-time analysis impossible for researchers and data-heavy enterprises. The Solution: I engineered a highly optimized, parallelized architecture to solve complex thermal dynamics. Instead of relying on a single-thread baseline, I architected a distributed-memory solver utilizing MPI (Message Passing Interface) across multiple nodes. To maximize single-node efficiency, I implemented thread-level parallelism using OpenMP. Finally, the core mathematical flux kernels were accelerated using GPU computing. This multi-layered approach (MPI + OpenMP + GPU) drastically reduced execution time, eliminated memory leaks, and ensured 100% hardware utilization. Key Deliverables Included: Standard C++ Baseline formulation. Distributed-memory solver (mpi_dist_solver.cpp). Shared-memory multi

High-Performance Computing: Thermal

The Problem: Standard CPU computations for complex thermal and flux algorithms often bottleneck system performance. Simulating large-scale grids sequentially takes massive amounts of time and memory, making real-time analysis impossible for researchers and data-heavy enterprises. The Solution: I engineered a highly optimized, parallelized architecture to solve complex thermal dynamics. Instead of relying on a single-thread baseline, I architected a distributed-memory solver utilizing MPI (Message Passing Interface) across multiple nodes. To maximize single-node efficiency, I implemented thread-level parallelism using OpenMP. Finally, the core mathematical flux kernels were accelerated using GPU computing. This multi-layered approach (MPI + OpenMP + GPU) drastically reduced execution time, eliminated memory leaks, and ensured 100% hardware utilization. Key Deliverables Included: Standard C++ Baseline formulation. Distributed-memory solver (mpi_dist_solver.cpp). Shared-memory multi

High-Performance Computing: Thermal

The Problem: Standard CPU computations for complex thermal and flux algorithms often bottleneck system performance. Simulating large-scale grids sequentially takes massive amounts of time and memory, making real-time analysis impossible for researchers and data-heavy enterprises. The Solution: I engineered a highly optimized, parallelized architecture to solve complex thermal dynamics. Instead of relying on a single-thread baseline, I architected a distributed-memory solver utilizing MPI (Message Passing Interface) across multiple nodes. To maximize single-node efficiency, I implemented thread-level parallelism using OpenMP. Finally, the core mathematical flux kernels were accelerated using GPU computing. This multi-layered approach (MPI + OpenMP + GPU) drastically reduced execution time, eliminated memory leaks, and ensured 100% hardware utilization. Key Deliverables Included: Standard C++ Baseline formulation. Distributed-memory solver (mpi_dist_solver.cpp). Shared-memory multi

High-Performance Computing: Thermal

13729533
Designed and implemented a high-performance, dependency-free module to fetch and process data from S3-compatible storage. Key Contributions: Zero-Dependency Architecture: Replaced heavy AWS SDKs with a custom, lightweight HttpClient implementation using pre-signed URLs. Smart Auto-Decompression: Built an intelligent stream handler that automatically detects and decompresses .tar.gz and .zip archives on the fly. Robust Data Handling: Implemented generic, case-insensitive JSON deserialization to ensure reliability across different API data formats. Optimization: Reduced memory footprint by using streaming instead of loading full files into memory.

Lightweight S3 Object Storage Helper

13723443
I redesigned the user interface of an existing browser-based 2D CAD tool (Excedify) to make it feel like a professional engineering application comparable to Autodesk Inventor and Fusion 360. Key Contributions: Complete UI Overhaul: Implemented a sleek Dark Mode theme to reduce eye strain for engineers. Improved UX: Organized tools into intuitive Left (Creation) and Right (Properties/GD&T) panels, reducing clutter. Responsive Layout: Added a floating bottom toolbar for navigation (Pan/Zoom) to maximize the drawing workspace. SEO Optimization: Updated HTML <head> with proper meta tags, Open Graph (OG) tags, and structured data to improve search engine visibility and social sharing previews. The design was selected as the winning entry and is now live. You can Check it here : https://tools.excedify.com/2d-cad/

Modern UI/UX Redesign for CAD Tool

13842836
The Problem: Standard CPU computations for complex thermal and flux algorithms often bottleneck system performance. Simulating large-scale grids sequentially takes massive amounts of time and memory, making real-time analysis impossible for researchers and data-heavy enterprises. The Solution: I engineered a highly optimized, parallelized architecture to solve complex thermal dynamics. Instead of relying on a single-thread baseline, I architected a distributed-memory solver utilizing MPI (Message Passing Interface) across multiple nodes. To maximize single-node efficiency, I implemented thread-level parallelism using OpenMP. Finally, the core mathematical flux kernels were accelerated using GPU computing. This multi-layered approach (MPI + OpenMP + GPU) drastically reduced execution time, eliminated memory leaks, and ensured 100% hardware utilization. Key Deliverables Included: Standard C++ Baseline formulation. Distributed-memory solver (mpi_dist_solver.cpp). Shared-memory multi

High-Performance Computing: Thermal

The Problem: Standard CPU computations for complex thermal and flux algorithms often bottleneck system performance. Simulating large-scale grids sequentially takes massive amounts of time and memory, making real-time analysis impossible for researchers and data-heavy enterprises. The Solution: I engineered a highly optimized, parallelized architecture to solve complex thermal dynamics. Instead of relying on a single-thread baseline, I architected a distributed-memory solver utilizing MPI (Message Passing Interface) across multiple nodes. To maximize single-node efficiency, I implemented thread-level parallelism using OpenMP. Finally, the core mathematical flux kernels were accelerated using GPU computing. This multi-layered approach (MPI + OpenMP + GPU) drastically reduced execution time, eliminated memory leaks, and ensured 100% hardware utilization. Key Deliverables Included: Standard C++ Baseline formulation. Distributed-memory solver (mpi_dist_solver.cpp). Shared-memory multi

High-Performance Computing: Thermal

The Problem: Standard CPU computations for complex thermal and flux algorithms often bottleneck system performance. Simulating large-scale grids sequentially takes massive amounts of time and memory, making real-time analysis impossible for researchers and data-heavy enterprises. The Solution: I engineered a highly optimized, parallelized architecture to solve complex thermal dynamics. Instead of relying on a single-thread baseline, I architected a distributed-memory solver utilizing MPI (Message Passing Interface) across multiple nodes. To maximize single-node efficiency, I implemented thread-level parallelism using OpenMP. Finally, the core mathematical flux kernels were accelerated using GPU computing. This multi-layered approach (MPI + OpenMP + GPU) drastically reduced execution time, eliminated memory leaks, and ensured 100% hardware utilization. Key Deliverables Included: Standard C++ Baseline formulation. Distributed-memory solver (mpi_dist_solver.cpp). Shared-memory multi

High-Performance Computing: Thermal

The Problem: Standard CPU computations for complex thermal and flux algorithms often bottleneck system performance. Simulating large-scale grids sequentially takes massive amounts of time and memory, making real-time analysis impossible for researchers and data-heavy enterprises. The Solution: I engineered a highly optimized, parallelized architecture to solve complex thermal dynamics. Instead of relying on a single-thread baseline, I architected a distributed-memory solver utilizing MPI (Message Passing Interface) across multiple nodes. To maximize single-node efficiency, I implemented thread-level parallelism using OpenMP. Finally, the core mathematical flux kernels were accelerated using GPU computing. This multi-layered approach (MPI + OpenMP + GPU) drastically reduced execution time, eliminated memory leaks, and ensured 100% hardware utilization. Key Deliverables Included: Standard C++ Baseline formulation. Distributed-memory solver (mpi_dist_solver.cpp). Shared-memory multi

High-Performance Computing: Thermal

The Problem: Standard CPU computations for complex thermal and flux algorithms often bottleneck system performance. Simulating large-scale grids sequentially takes massive amounts of time and memory, making real-time analysis impossible for researchers and data-heavy enterprises. The Solution: I engineered a highly optimized, parallelized architecture to solve complex thermal dynamics. Instead of relying on a single-thread baseline, I architected a distributed-memory solver utilizing MPI (Message Passing Interface) across multiple nodes. To maximize single-node efficiency, I implemented thread-level parallelism using OpenMP. Finally, the core mathematical flux kernels were accelerated using GPU computing. This multi-layered approach (MPI + OpenMP + GPU) drastically reduced execution time, eliminated memory leaks, and ensured 100% hardware utilization. Key Deliverables Included: Standard C++ Baseline formulation. Distributed-memory solver (mpi_dist_solver.cpp). Shared-memory multi

High-Performance Computing: Thermal

13729533
Designed and implemented a high-performance, dependency-free module to fetch and process data from S3-compatible storage. Key Contributions: Zero-Dependency Architecture: Replaced heavy AWS SDKs with a custom, lightweight HttpClient implementation using pre-signed URLs. Smart Auto-Decompression: Built an intelligent stream handler that automatically detects and decompresses .tar.gz and .zip archives on the fly. Robust Data Handling: Implemented generic, case-insensitive JSON deserialization to ensure reliability across different API data formats. Optimization: Reduced memory footprint by using streaming instead of loading full files into memory.

Lightweight S3 Object Storage Helper

13723443
I redesigned the user interface of an existing browser-based 2D CAD tool (Excedify) to make it feel like a professional engineering application comparable to Autodesk Inventor and Fusion 360. Key Contributions: Complete UI Overhaul: Implemented a sleek Dark Mode theme to reduce eye strain for engineers. Improved UX: Organized tools into intuitive Left (Creation) and Right (Properties/GD&T) panels, reducing clutter. Responsive Layout: Added a floating bottom toolbar for navigation (Pan/Zoom) to maximize the drawing workspace. SEO Optimization: Updated HTML <head> with proper meta tags, Open Graph (OG) tags, and structured data to improve search engine visibility and social sharing previews. The design was selected as the winning entry and is now live. You can Check it here : https://tools.excedify.com/2d-cad/

Modern UI/UX Redesign for CAD Tool

5.0 · 10 Reviews
10 Reviews
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Flag of

Guowang M.

5.0

$100 USD

5 minutes ago

Find AI Researchers in Universities and Tech Industry

good job. well done.

Academic Research
Artificial Intelligence
Data Scraping
Email Campaign
Email Marketing

+1 more

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Cool .

5.0

$150 USD

23 days ago

Fixes for www.coolbreezecars.com

Very professional, great and fast communication, he understand exactly what needs to be done. Highly recommended.

Python
Website Design
Android
Web Scraping

+1 more

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Flag of

Ariel M.

5.0

$500 USD

2 months ago

Full-Stack Module Development & System Integration

Un dev super recomendable

Python
Android
Web Scraping
Node.js

+1 more

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Ariel M.

5.0

$666 USD

2 months ago

Force External USB UVC Camera as Only System Camera (Pixel 8a – EvolutionX 15)

Muy buena experiencia. Muy profesional y con mucho conocimiento

Android
Android App Development
Android SDK
C Programming
Debugging

+5 more

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Marcus V.

5.0

$100 AUD

5 months ago

Lead Generation Web Scraper

Gave me exactly what I wanted + more

BeautifulSoup
Data Extraction
Data Mining
Java
Python

+3 more

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Experience

Cheif Technology Officer

Dec, 2025 - Present

•

5 months, 8 days

Ai & Beyond

Dec, 2025 - Present

•

5 months, 8 days

Leading the technological vision and execution at Ai & Beyond, I oversee the development of scalable AI solutions and infrastructure. I manage a cross-functional engineering team to deliver cutting-edge products while aligning technical roadmaps with core business objectives. My focus is on driving innovation, optimizing R&D processes, and ensuring our tech stack remains at the forefront of the industry

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Lahore, Pakistan

Dec, 2025 - Present

•

5 months, 8 days

Cyber Security Team Lead

Mar, 2025 - Jul, 2025

•

4 months

IPS technologies

Mar, 2025 - Jul, 2025

•

4 months

As a Cybersecurity Team Lead, my focus shifted from just technical fixes to building a resilient team. I prioritize mentoring, fostering communication, and staying ahead of evolving threats through continuous learning. It's a demanding role of constant vigilance, where strategic vision and clear communication are as vital as technical skills. Every day is a new challenge, pushing us to innovate and protect.

Flag of

Lahore, Pakistan

Mar, 2025 - Jul, 2025

•

4 months

Education

University of Management and Technology

2022 - 2025

•

3 years

BS Computer Science

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Pakistan

2022 - 2025

•

3 years

Superior College

2020 - 2022

•

2 years

Intermediate

Flag of

Pakistan

2020 - 2022

•

2 years

Qualifications

Certified Ethical Hacker - V12

2025

EC-Council

(CEH) by EC-Council, trained in advanced penetration testing, vulnerability assessment, and real-world hacking techniques. Skilled in identifying and securing vulnerabilities to strengthen systems against cyberattacks.

2025

CyberSecurity

2025

Google

Google Cybersecurity Certification holder with hands-on expertise in network security, threat analysis, and incident response. I help clients safeguard their systems and data using industry-standard tools and best practices.

2025

IT Support

2024

Google

Practical skills in troubleshooting, system administration, networking, and customer support. Experienced in diagnosing technical issues and implementing effective solutions to ensure smooth IT operations.

2024

Certifications

AI Training - Freelancer Global Fleet

AI Training - Freelancer Global Fleet

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