Muhammad is currently unavailable
Muhammad Ali Shahzad
Verified Expert in Engineering
Software Engineer and Developer
Hamburg, Germany
Toptal member since June 3, 2025
Bio
Muhammad is a senior back-end engineer with 10+ years of experience building scalable systems using Python, FastAPI, Apache Kafka, gRPC, and Kubernetes. He's led cloud-native development on AWS and GCP, designed microservices, and built AI-driven document processing pipelines. From scaling startups to delivering for Fortune 500s, Muhammad has focused on clean architecture, reliability, and business impact.
Portfolio
Lufthansa
Python, Large Language Models (LLMs), Artificial Intelligence (AI)...
Deepreader
Artificial Intelligence (AI), BERT, Computer Vision, Analysis...
German Research Center for Artificial Intelligence GmbH (DFKI)
Artificial Intelligence (AI), BERT, Computer Vision, Analysis, LSTM...
Experience
- Python 3 - 11 years
- Deep Learning - 10 years
- RabbitMQ - 7 years
- Flask - 7 years
- FastAPI - 6 years
- Kubernetes - 6 years
- Amazon Web Services (AWS) - 4 years
- Large Language Models (LLMs) - 3 years
Preferred Environment
Windows, Ubuntu, Linux, MacOS
The most amazing...
...project I've created is a startup that I successfully scaled from 4 to 120 FTE in four years, driving growth, building culture, and leading every stage.
Work Experience
Senior Software Architect
2025 - PRESENT
Lufthansa
- Engineered ETL pipelines with Slack-integrated incident notifications and led Cloud Spanner schema migrations, cutting mean time to detection for production data issues.
- Built an AI-powered documentation search platform unifying 20+ internal projects, accelerating engineer onboarding, and reducing cross-team context-switching.
- Developed ETL transformation pipelines and production-grade Python services on GCP (Cloud Run, Pub/Sub, BigQuery, and Firestore), improving reliability of data delivery across the board.
Technologies: Python, Large Language Models (LLMs), Artificial Intelligence (AI), Google Cloud Platform (GCP), Cloud Architecture, Software Architecture, Cloud Run, Kubernetes, CI/CD Pipelines, Terraform, Vibe Coding, Technical Leadership, Jira, Confluence, Pub/Sub, MongoDB, BigQuery, Privileged Access Management (PAM)
Senior Software Engineer
2020 - 2025
Deepreader
- Built distributed Python back ends with RabbitMQ for long-running tasks, FastAPI for APIs, and configurable pipelines for document processing and analysis. Used Pydantic for data validation and clean request/response models.
- Automated document classification and extraction. Developed an AI-driven automation system for an accounting firm, reducing manual effort, improving verification efficiency, and achieving significant cost and time savings.
- Developed a document correlation system. Matched financial documents to detect anomalies, accelerating review time by 40%. Integrated ABBYY, OmniPage, and Tesseract with NER models and implemented a voting mechanism, boosting accuracy by 7%.
- Built a stateless, scalable document processing pipeline and dynamically distributed pages across Kubernetes pods, achieving near-linear scalability and reducing processing time.
- Migrated long-running tasks to RabbitMQ. Enhanced fault tolerance, auto-rescheduling, and workload distribution. Introduced priority-based task handling. Ensured that critical documents were processed first, improving efficiency and reliability.
- Integrated Loki and Grafana. Enabled real-time monitoring, significantly reducing turnaround time for issue detection and resolution.
- Implemented Agile Scrum, improved team communication, and reduced ticket resolution time by 20%.
- Created client-facing documentation and presentations. Developed materials for progress updates, release notes, and onboarding, ensuring seamless product adoption.
- Integrated LLMs and VLMS (Google Gemini, OpenAI, and Anthropic) using Python as the base language for the business logic.
Technologies: Artificial Intelligence (AI), BERT, Computer Vision, Analysis, Optical Character Recognition (OCR), LSTM, Large Language Models (LLMs), Amazon Web Services (AWS), Google Cloud Platform (GCP), RabbitMQ, FastAPI, Flask, Kubernetes, Helm, Terraform, Hetzner, ABBYY, Tesseract, Deep Learning, Scalable Architecture, Distributed Software, MongoDB, PostgreSQL, RESTFul APIs, Docker Compose, OmniPage, Loki, Grafana, API Integration, GitHub, AWS Lambda, APIs, Python, Python 3, Software Architecture, System Design
Software Engineer
2019 - 2020
German Research Center for Artificial Intelligence GmbH (DFKI)
- Developed cutting-edge deep learning methods for table detection, structure extraction, and entity extraction in scanned documents.
- Designed deep learning-based models for identifying and structuring tabular data in document images, significantly improving accuracy over traditional OCR methods.
- Implemented NER-based entity extraction for structured and semi-structured forms, leveraging computer vision and deep learning techniques.
- Explored and combined classical CV techniques with modern deep learning to enhance document understanding tasks.
- Optimized production-grade deep learning pipelines for training, serving, and inference in large-scale environments.
- Built distributed microservices using Docker, RabbitMQ, and gRPC, enabling seamless model integration and scaling.
- Worked with ABBYY FineReader and Tesseract to extract structured data from scanned documents.
- Pioneered a hybrid deep learning and hand-crafted feature approach for table detection, achieving state-of-the-art performance on the UNLV dataset.
- Developed a GRU-based recurrent neural network for row-column classification, outperforming traditional OCR-based table parsing solutions.
- Created highly scalable deep learning model deployment pipelines in Python to deploy Bert, Roberta, LayoutLM, and ResNet models.
Technologies: Artificial Intelligence (AI), BERT, Computer Vision, Analysis, LSTM, Deep Learning, Amazon Web Services (AWS), ABBYY, FastAPI, Flask, Distributed Software, Google Cloud Platform (GCP), Helm, Hetzner, Kubernetes, Optical Character Recognition (OCR), MongoDB, Java, C#.NET, Table detection, Tables, Docker, Microservices, Linux, Ubuntu, Docker Compose, API Integration, Python, GitHub, APIs, Python 3, Python API
Partner and Technical Lead
2016 - 2019
VisionX Technologies
- Scaled a startup from four to 120 employees, driving AI-led digital innovation.
- Collaborated with Fortune 500 companies and high-growth unicorns, ensuring a seamless bridge between business strategy, technical execution, and client success.
- Led R&D teams to create and deliver cutting-edge AI solutions that transformed client operations.
- Conducted market analysis and technical research, identifying strategic opportunities for AI adoption.
- Established engineering best practices to ensure scalable, high-quality software delivery.
- Led the development and deployment of a building and campus management solution for PackageX, successfully scaling it to 18 WeWork locations in New York City.
- Bridged business and engineering teams for Staples Inc., driving award-winning projects and contributing to a patented solution. Optimized quotation systems, reducing multi-day turnaround times to real-time product pricing.
- Enhanced Pitney Bowes's mail-room handheld systems, fixing critical production issues, and significantly improving speed and accuracy.
- Collaborated with Vic AI, a Norwegian startup, to develop a state-of-the-art NER system for invoice processing. Ensured technical alignment with business goals, enhancing automation accuracy.
- Directed AI-driven improvements for music note detection and recognition software for Lugert Verlag. Optimized deep learning models, increasing accuracy by 15%.
Technologies: Python 3, Android, iOS, Team Leadership, Scrum, Agile, Amazon Web Services (AWS), Technical Leadership, Product Strategy, Product Roadmaps, System Design, Communication, Customer Research, Computer Vision, Deep Learning, Optical Character Recognition (OCR), Named-entity Recognition (NER), Documentation, C++, Ubuntu, Linux, Docker Compose, Artificial Intelligence (AI), API Integration, Python, GitHub, APIs
Experience
VisionQuery, an AI-powered Image Retrieval Platform
This project involved designing and implementing an advanced visual language model (VLM) image retrieval system. The language of choice for this project was Python. I implemented an advanced contrastive language-image pre-training (CLIP) VLM to generate image embeddings from visual content. I created natural language search capabilities, allowing users to find images by describing their content, and leveraged Pinecone to store and efficiently query image embeddings.
In addition, I managed all infrastructure through Terraform, including Google Cloud SQL, Cloud Run, and Google Cloud Storage, and implemented a GitHub Actions workflow for automated testing, building, and deployment. We used dual authentication with JSON Web Tokens (JWT) for admin access and API keys for programmatic usage, and we designed modular services that communicated through well-defined API endpoints.
The system also leveraged team isolation, password/API key hashing, granular permission controls, REST API with FastAPI, Google Cloud Storage integration, API key authentication, and containerization with Docker. I also implemented advanced image understanding with the CLIP model and semantic image search capabilities.
In addition, I managed all infrastructure through Terraform, including Google Cloud SQL, Cloud Run, and Google Cloud Storage, and implemented a GitHub Actions workflow for automated testing, building, and deployment. We used dual authentication with JSON Web Tokens (JWT) for admin access and API keys for programmatic usage, and we designed modular services that communicated through well-defined API endpoints.
The system also leveraged team isolation, password/API key hashing, granular permission controls, REST API with FastAPI, Google Cloud Storage integration, API key authentication, and containerization with Docker. I also implemented advanced image understanding with the CLIP model and semantic image search capabilities.
Leetcode
I solved LeetCode problems to strengthen and demonstrate my Python programming skills, focusing on algorithms, data structures, and problem-solving techniques to build efficient and optimized solutions.
Education
2015 - 2019
Master's Degree in Computer Science
National University of Sciences and Technology (NUST) - Islamabad, Pakistan
2009 - 2013
Bachelor's Degree in Software Engineering
Foundation University Islamabad - Islamabad, Pakistan
Certifications
MARCH 2025 - PRESENT
ChatGPT Prompt Engineering for Developers
DeepLearning.AI
FEBRUARY 2025 - PRESENT
Introduction to Open Source License Compliance Management (LFC193)
The Linux Foundation
MAY 2024 - PRESENT
Deutsch B1
TELC
MARCH 2015 - PRESENT
Function-calling and Data Extraction with LLMs
DeepLearning.AI
Skills
Libraries/APIs
LSTM, Python API
Tools
RabbitMQ, Helm, ABBYY, Named-entity Recognition (NER), ChatGPT, Docker Compose, GitHub, Terraform, Visual Language Models (VLMs), OmniPage, Loki, Grafana, Jira, Confluence, BigQuery
Languages
Python 3, Python, C++, C, Python 2, Java, C#.NET
Frameworks
Flask
Paradigms
Microservices, Software Testing, Unit Testing, Scrum, Agile
Platforms
Kubernetes, AWS Lambda, Amazon Web Services (AWS), Google Cloud Platform (GCP), Docker, Linux, Ubuntu, Android, iOS, Windows, MacOS, Cloud Run
Storage
Redis, MongoDB, PostgreSQL
Other
Artificial Intelligence (AI), Optical Character Recognition (OCR), Large Language Models (LLMs), FastAPI, Tesseract, Scalable Architecture, Distributed Software, Table detection, Tables, Natural Language Processing (NLP), GitHub Actions, APIs, BERT, Residual Neural Networks (ResNets), Analysis, Computer Vision, Hetzner, Deep Learning, RESTFul APIs, Team Leadership, Technical Leadership, Product Strategy, Product Roadmaps, System Design, Communication, Customer Research, Documentation, Pinecone, OpenAI, German, Open Source, Computer Science, Software Engineering, API Integration, Software Architecture, Cloud Architecture, CI/CD Pipelines, Vibe Coding, Pub/Sub, Privileged Access Management (PAM)
Collaboration That Works
How to Work with Toptal
Toptal matches you directly with global industry experts from our network in hours—not weeks or months.
1
Share your needs
Discuss your requirements and refine your scope in a call with a Toptal domain expert.
2
Choose your talent
Get a short list of expertly matched talent within 24 hours to review, interview, and choose from.
3
Start your risk-free talent trial
Work with your chosen talent on a trial basis for up to two weeks. Pay only if you decide to hire them.
Top talent is in high demand.
Start hiring