Josh Niemelä

jniemela.dk

josh@argmin.dk joshniemela joshniemela +45 50 32 45 42 /in/joshniemela /in/joshniemela

I act as an effective link between technical and non-technical stakeholders to ensure that the end product meets all requirements and that the project maximises organisational value. With my extensive knowledge in machine learning and computer science, I provide valuable insights and solutions to complex problems and have a more holistic view of the project than a typical project manager or developer.

Experience

Consultant / Backend Developer

2023 ⟶

Two Scenarios

12/2024 - 12/2024

  • Completed an urgent task by developing a concurrent program within a week to retrieve and process pharmaceutical data from the Danish Medicines Agency (Lægemiddelstyrelsen) thus ensuring the customer met their critical project deadlines.

Pingo Documents

09/2024 ⟶

  • Performed a performance audit of a problematic part of the website, and reduced the number of database queries by 92% and decreased load times by more than 15x.
  • Implemented a vector-based search algorithm to retrieve names from the EU financial sanctions list.
  • Responsible for coordinating a team of five consultants, ensuring that the client's wishes are implemented and communicated.
  • Designed new architecture that can be incrementally implemented to fix fundamental limitations in the existing codebase.

AI Estate

09/2023 - 09/2024

  • Worked as a contractor for Pingo Documents (working with Pingo directly after 09/2024) and was responsible for implementing and designing the heavier tasks, 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.
  • Performed an audit of website performance, found and fixed several problems and inefficiencies reducing load time by 318%.

AI Estate

06/2023 - 07/2023

  • Responsible for the setup of all IT infrastructure of an early stage startup (GCP, GitHub and CI/CD), and ensured that good programming practices were followed by the team.
  • Provided consultation on the choice of technologies and architecture for the initial MVP.
  • Rapidly developed an MVP in Python using FastAPI and SQLite, in three weeks, to interface with LLMs and other services as well as harvest relevant information from Tinglysning.dk.

Promilist

01/2023 - 03/2023

  • Migrated a Python codebase to Julia, improving readability, maintainability and performance.
  • Developed code to calculate fuel-optimal paths for naval vessels, by graph-traversing space-time graphs on a geodesic map outperforming the existing solution in Python by 5x.
  • 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 in two months.

KU Courses - Project

05/2023 ⟶

  • Lead developer of a open source project with multiple contributors.
  • Used SEO and grassroots marketing to increase the monthly active users from 0 to 200.
  • Developed a event-driven microservice based SPA in Typescript, Clojure and Rust which asynchronously scrape pages, stores and displays course information from KU's course catalogue with significant performance (3x-15x shorter latency) and UX improvements over the existing official solution.
  • 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

BSc in Machine Learning and Data Science

08/2022 - 06/2025

Copenhagen University (DIKU)
  • 7-point scale average of 11.0 / 12.0.
  • Wrote my bachelor thesis on topological deep learning and graph representation learning, which then evolved into a reproducibility study of state of the art graph machine learning models. It can be found on GitHub, BottlenecksWithinGNNs.

Skills

  • C#/.NET, TypeScript/Svelte, Python, Rust, Julia, Clojure
  • SDLC, AGILE, project management, software architecture, risk analysis
  • GitHub Actions, Docker Compose, VPS and cloud hosting
  • Machine learning, computer vision, deep learning, image processing, NLP, data science