I bridge the gap between technical and non-technical stakeholders to deliver projects that effectively incorporate business domain knowledge whilst minimising technical debt or vendor lock-in. I specialise in implementing performant and robust APIs, integrations and other services. Additionally, I have extensive experience developing GDPR and AML compliant systems, conducting security audits and implementing systems that safely handle sensitive information.

Experience

Consultant & Lead Developer - argmin Consulting

2023 ⟶

argmin Consulting

03/2026 ⟶

  • Built and managed a self-hosted Kubernetes cluster to host a private OCI registry, code repositories, CI/CD pipeline and websites, ensuring cluster data is encrypted E2E and at rest.
  • Built tooling to swiftly deploy the LGTM+Otel stack across Docker Compose and Kubernetes environments, providing sensible performance-oriented defaults, preconfigured dashboards.

Pingo Documents

09/2024 ⟶

  • Designed and implemented a new DSL from scratch, formalising loosely defined syntax, implementing support for vectorised operations, static type checking, and improving testability by reducing cross-component dependencies.
  • Implemented a modern data encryption system, comprised of multiple microservices, significantly improving data security, auditability and robustness to various cyberattacks.
  • Replaced the main product backend with a domain-driven, event-sourced architecture. This solved fundamental limitations in the existing backend, improved performance and reduced the requirement for manual testing overhead by enforcing domain invariants.
  • Conducted a performance audit on a problematic and often used website component, and reduced the number of database queries in MSSQL by 92% and decreased load times by more than 15x.
  • Implemented integrations to eTL (Danish land registry) and CVR (Danish Central Business Register), reducing the need for manual data entry.

Two Scenarios

12/2024 - 12/2024

  • Built an integration for Two Scenarios to continuously transfer pharmaceutical pricing data into a database within a week, helping them to meet a crucial deadline. See case study

AI Estate

06/2023 - 09/2024

  • Worked as a contractor for Pingo Documents (working with Pingo directly after 09/2024) and was responsible for completing the more complex tasks, as well as guiding and assisting two other developers in smaller tasks.
  • Responsible for CI/CD, DevOps, security and ensuring the team met deadlines for client deliverables.
  • Implemented major updates to UX and features, by making the frontend more responsive and giving it a more modern look as well as improving the previously unintuitive user flow.
  • Made a service to perform information retrieval on real-estate documents using OCR and a language model.

Promilist

01/2023 - 03/2023

  • Migrated a Python codebase to Julia, improving inference speed by 5x. This was used to calculate fuel-optimal paths for naval vessels, by graph-traversing space-time graphs on a geodesic map.
  • Implemented an API in Genie.jl to interface with the existing backend.

Machine Learning Developer - Juristic ApS

08/2022 - 02/2023

  • Developed a program to automatically digitalise handwritten relational charts, using a combination of computer vision, clustering and morphological image processing.

KU Courses - Project

05/2023 ⟶

  • Developed a event-driven microservice based SPA in Typescript, Rust and Clojure, using scraped data from Copenhagen University's course catalogue. Achieved 15x faster searches and consolidated information that previously required three separate websites open into a unified interface.
  • ONNX was used to perform vector-based search of courses with a pre-trained sentence transformer model. This approach yielded more robust and relevant search results compared to the existing solution.

Education

B.Sc in Machine Learning and Data Science

08/2022 - 19/06/2025

Copenhagen University (DIKU)
  • 7-point scale average of 10.9 / 12.0.
  • Took 30 ECTS of M.Sc courses in randomised and approximation algorithms, computational geometry, probabilistic machine learning and IT project management.
  • Wrote a bachelor's thesis on topological deep learning and graph representation learning, which evolved into a reproducibility study of state of the art GNN models. It can be found on Forgejo, BottlenecksWithinGNNs.

Skills

  • Languages: C# (.NET), GoLang, Rust, Svelte/TypeScript, Python, Clojure (JVM), Julia
  • DevOps and Tools: Kubernetes, CI/CD, HashiCorp Vault, S3, VPS & cloud hosting (Hetzner, GCP), Azure AI