Bavaria, Germany
I am Ali Muhammad, a Software & AI Engineer currently pursuing an M.Sc. in Artificial Intelligence Engineering at the University of Passau.
Professional background
Built an Agentic AI pipeline using LLMs to automatically generate clinical summaries and reduce documentation time by 50%
Developed a RAG pipeline with vector search and LLMs for context-aware clinical decision support
Connected microservices using RabbitMQ and event-driven architecture to improve real-time inference performance by 35%
Engineered cron-based workflows in Node.js microservices to automate recurring processes and improve operational accuracy by 40%
Designed and integrated RESTful APIs using a Factory Pattern abstraction layer, enabling standardized third-party integrations and scalable microservice communication
Secured microservices using JWT authentication, Kong API Gateway rate limiting, and RBAC, reducing unauthorized access by 50%
Boosted platform user retention by building an automated email notification microservice that triggers personalized engagement at key user milestones
Improved API reliability and data integrity by debugging production microservices and implementing request validation and input sanitization
Reduced mean time to resolution (MTTR) by collaborating with cross-functional teams to triage production incidents and deploy targeted fixes, improving release stability
Delivered a fully functional simulator for a national instant payment system by implementing RESTful APIs aligned with official documentation and interoperability protocols
Validated real-time P2P fund transfer flows by implementing end-to-end payment logic based on production-grade sequence diagrams, ensuring system reliability
Enhanced payment security by contributing to backend API design with certificate-based encryption and decryption mechanisms for secure transaction processing
Worked within a production MERN stack codebase, gaining hands-on experience with professional code structure, team conventions, and end-to-end development workflows
Contributed to the backend architecture of an HR Management System to streamline project allocation, resource planning, and workforce operations
Improved data consistency and cross-module communication by designing and integrating RESTful APIs for project and resource tracking workflows
Education
University of Passau
Current graduate focus on AI engineering, applied machine learning, intelligent systems, and building software that can use data and AI responsibly.
FAST - National University of Computer and Emerging Sciences
Built the core computer science base behind my engineering work: programming, databases, software design, algorithms, and backend development fundamentals.
Selected work
These projects combine software engineering discipline with modern AI workflows, from structured frontend delivery to document retrieval and autonomous email analysis.
Designed and shipped a Next.js static social media app using a GitHub-speckit with Codex as the coding agent throughout.
Both projects below focus on automation: retrieval, extraction, reasoning, persistence, and clear system outputs.
Retrieved passages are condensed into a contextual response.
Designed a RAG system over lecture notes and textbooks so students can query multiple educational PDFs and receive fast, context-aware answers grounded in their course material.
View repositorySummary, questions, and status saved to MongoDB.
Built an agent that reads emails, writes concise summaries, detects direct and indirect questions, stores the result in MongoDB, and uses an LLM to decide whether the email needs a response.
RAG pipelines, LLM agents, vector search, summaries, extraction, classification, and decision workflows shaped with practical software constraints.
REST APIs, microservices, event-driven systems, scheduled workflows, SQL optimization, authentication, and maintainable service architecture.
Docker, AWS, Azure, CI/CD habits, system reliability, and clean handoff from implementation to deployment-ready software.
Technical toolkit
Certifications
Cloud fundamentals covering AWS services, architecture, security, and deployment concepts.
Hands-on learning around AI agents, multi-step LLM workflows, and tool-using systems.
Applied Python, data manipulation, tabular analysis, and practical notebook workflows.
Programming and database querying practice for data-heavy product and backend work.
Languages
Contact
I am open to software engineering, backend, full-stack, AI engineering, and LLM application roles.