Denis XhabrahimiSoftware & Data Engineer
I build production software and data-intensive systems, from AI automation and full-stack platforms to distributed and high-performance computing.
MSc Data Science, University of Basel
MSc Data Science
University of Basel · BSc Computer Science
Software running in real organizations
Operations platform at FAB Construction · AI support agent at RYCO
73× GPU speedup
1D Earth Mover's Distance compute phase, measured on an A100
Distributed & high-performance computing
MPI · CUDA · OpenMP, benchmarked on Basel's SciCORE cluster
Selected Work
Three problems, end to end
An operations platform for a construction business, an AI intake agent for a real helpdesk, and a performance study that picked its parallelization strategy on measured runtime and energy.
Replacing spreadsheet-driven inventory with a centralized operations platform
FAB Construction Inventory Management System
- The problem
- A construction company tracked stock, tools and job status across spreadsheets and informal messages — so nobody could reliably answer what was in stock, where it was, or which work was blocked.
- What changed
- One system for products, multi-location stock, suppliers, stock movements, work processes and role-based access, replacing the spreadsheet-and-messages workflow across FAB's sites.
Automating unstructured IT support intake and ticket routing with AI
AI-Powered IT Support Agent
- The problem
- IT requests reached RYCO's helpdesk as ad-hoc emails and Teams messages with no categorization, routing or SLA tracking, so every request began with manual triage.
- What changed
- A guided conversation that turns a vague message into a validated, categorized, routed ticket with an automatic confirmation — deployed in Teams at RYCO, and rebuilt as a public live demo carrying no organizational data.
Choosing a parallelization strategy on measured runtime and energy, not intuition
High-Performance 1D Earth Mover's Distance
- The problem
- Distribution-comparison workloads do not scale sequentially, and the strategy that looks fastest on paper is often neither the fastest nor the cheapest one in practice.
- What changed
- Measured on Basel's SciCORE cluster: 73x for 1D EMD's compute phase on a single A100 and 4,245x for matrix multiplication on MPI+CUDA — alongside one case where OpenMP used ~91x less energy than the distributed version.
Capabilities
What I'm hired to do
Three areas, each backed by work you can read in full on this site.
AI & Automation
Turning unstructured input into structured, routed, actionable work.
Conversational intake, agent workflows and automation that sit inside the tools an organization already uses — plus the applied ML work behind them, including privacy-preserving training and evaluation.
Data & Software Platforms
Backends, APIs and data models that hold an operation together.
Full-stack business systems built around real operational entities — inventory, locations, suppliers, stock movements, permissions — with normalized schemas, authenticated APIs and interfaces that non-technical staff actually use.
Distributed & Performance-Critical Systems
Systems where compute cost, scale and execution model are the design problem.
Algorithms implemented across shared-memory, distributed-memory and GPU execution models, then benchmarked on a production HPC cluster for both runtime and energy-to-solution — so the strategy gets chosen on measurements rather than convention.
Experience
Built inside real organizations
The AI support agent and the inventory platform both came out of the roles below — problems I found while working there, not briefs handed to me.
September 2023 – January 2026
Technology Officer
RYCO (Regional Youth Cooperation Office)
Designed and built an AI-powered IT support agent using Microsoft Copilot Studio and Power Automate, automating conversational ticket intake, categorization, routing and confirmation for RYCO's helpdesk.
October 2021 – September 2023
Junior IT Consultant
FAB Construction
Deployed an Enterprise Resource Planning (ERP) system and Inventory Management System (IMS), increasing the working environment's efficiency.
Contact
Let's talk.
Hiring for AI, data or platform engineering — or building something technically difficult? Tell me what you are working on.
- AI & data engineering
- Forward-deployed / customer-facing engineering
- Distributed systems
- Applied AI & automation
- Data platforms