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---
title: "CV"
---
### About me
I like to tinker with computers/software, learning about good software architecture, and
mathematics. I am captivated by the beauty of functional programming and I am going
down the rabbit hole of learning some category theory for the sake of it.
My current long term goal is to finish developing/deploy my stock trading system.
In the mean time, I am looking for work where I can enhance my functional
programming skills and generate value for the shareholders of the company.
### Technical Skills
A short summery of some of my most relevant technical skills.
#### Programming
| Language | Level |
| -------- | ------------ |
| C | Superficial |
| Fennel | Fluent |
| Haskell | Intermediate |
| Lua | Fluent |
| Python | Fluent |
| Rust | Basic |
| Shell | Fluent |
| VHDL | Basic |
Haskell is a language I want to master. I find Rust's ownership model
interesting and a language I will explore more.
#### Markup
| Language | Level |
| -------- | ------------ |
| HTML | Proficient |
| LaTeX | Fluent |
| markdown | Fluent |
#### Databases
| Type | Implementation |
| ------------ | --------------------- |
| Cache | Redis |
| Graph | ArangoDB |
| Index | Elasticsearch |
| Message bus | Kafka, SQS |
| NoSQL | MongoDB |
| SQL | Postgres, TimescaleDB |
I would like to try out [Nats](https://nats.io/) as a message broker,
[skytable](https://github.com/skytable/skytable) as NoSQL database,
and [meilisearch](https://www.meilisearch.com/) for indexing.
#### Software
Here is a collection of software that I have interacted with that might be
worth mentioning.
| Name | Level |
| ---------- | ------------ |
| CircleCI | Proficient |
| Docker | Fluent |
| Git | Fluent |
| Helm | Proficient |
| Kubernetes | Proficient |
| Neomutt | Proficient |
| Neovim | Fluent |
| Tekton | Proficient |
| Terraform | Intermediate |
| Tmux | Intermediate |
I have plans on learning [Nomad](https://www.nomadproject.io), as this seems to
be a better alternative of k8s. I am also moving to [podman](https://podman.io/)
next time I work with containers in my spare time. I am also keen on working
more with service meshes, to improve my knowledge of their capabilities.
#### Operating Systems
I use Artix Linux as my daily OS. However, I would like to transition to OpenBSD
soon. But, I am a bit afraid of the lack of support for Nvidia GPUs.
Unfortunately, I use macOS for work.
#### Workflow
I use Neovim for all development, together with dwm as a tiling
window manager, and st as the terminal of choice. I am very happy with this
setup, but would like to improve the workflow with increased tmux usage and
git worktrees, à la [ThePrimeagen](https://www.youtube.com/watch?v=GXxvxSlzJdI).
I use a [ferris sweep](https://github.com/davidphilipbarr/Sweep) keyboard with
[Colemak Mod-DH](https://colemakmods.github.io/mod-dh/) layout. I moved on from
qwerty as I do not believe that you should go through life using suboptimal
solutions from the past, just because you cannot bother learning something new.
#### Architecture
Good software is like physics, it should avoid too much complexity, like von
Neumann famously stated, *"With four parameters I can fit an elephant, and with
five I can make him wiggle his trunk"*. I adhere by the [unix
philosophy](http://www.catb.org/esr/writings/taoup/html/ch01s06.html), as well
as the [suckless philosophy](https://suckless.org/philosophy/). I truly believe
that you develop better software by following these principles.
For designing and implementing distributed web service systems, I really like
the framework presented in the book *The Tao of Microservices*. Web services
should almost be provocatively small, most communication between services
should be asynchronous. It is important to reason and respect separation of concerns.
You should at all cost avoid building a distributed monolith with entangled dependencies.
#### Machine Learning
tbc...
#### Signal Processing
tbc...
#### Miscellaneous
| What | Level |
| --------- | ----- |
| Soldering | Basic |
### Experience
- Software Engineer. Nexure AB. Aug 2021 -- Present
- Develop and maintain microservices for payments and subscriptions.
- Take part in architectural design discussions.
- Participated in the code review process.
- Contribute to the infrastructure with updates to k8s resources and AWS
resource management via Terraform.
- Monitor logs for bugs in different environments, e.g. staging and
production.
*Keywords: Microservices, Kubernetes, Infrastructure, Helm, CI/CD, Kotlin,
Spring, AWS, Terraform*
- Machine Learning Engineer. Saab AB. Aug 2018 -- Aug 2021
- Intelligence Application. Aug 2020 -- Aug 2021
- Created data mining pipelines for extracting, cleaning, and creating
datasets for machine learning models, i.e. train/test sets.
- Was a driving force in making the system more asynchronous using
message passing between microservices, by deploying and maintaining a
Kafka instance. This improve the architecture by allowing multiple
services to subscribe to incoming sensor data and process the
information, instead of fetching the data from databases in batches.
- Developed a modular pipeline for training and evaluating deep learning
models with different architectures and/or losses. Automatic extraction
of the best model based on user defined metric, ready for serving.
- Built and deployed deep learning models for multi-modal trajectory
predictions in production.
- Took initiative and deployed and maintained a private Python Package
Index (PyPI) for all developers. Greatly improving the development
workflow, e.g. forcing versioning, and reducing/eliminating cross
dependencies between locally developed Python packages.
- Developed a graph algorithm for sensor fusion. Deployed it as a
microservice listing to incoming sensor data. This enabled more complex
pattern analysis in downstream services.
- Reduced the docker image size of the Python microservices from ~2 GB
to ~73 MB by utilizing multistage builds and alpine base images.
- Built pipelines for CI/CD and packages deployment in Tekton.
- With my docker images and pipelines we where able to reduce the
average build times from ~10-30 minutes down to seconds, mostly thanks
to improved caching capabilities.
*Keywords: Deep Learning, Python, Microservices, Kubernetes,
Infrastructure, Helm, CI/CD*
- Radar Warning Receiver. Aug 2018 -- Aug 2020
- Built simulation software for generating realistic signal
environments with both radar and/or communication signals. Implemented
the most common signal encoding for communications, as well as
basic to SOTA radar modulations. This enabled the team to develop and
evaluate different machine learning models and ideas.
- Researched machine learning models in different stages of the radar
warning receiver, with regards to compute and data limitations.
- Held in several presentations of machine learning papers in a company
reading group.
- Shared the knowledge of implementing and using machine learning to
multiple business areas within Saab.
*Keywords: Deep Learning, Signal Processing, Python, VHDL*
### Institutions
- M.Sc., Electrical Engineering. Kungliga Tekniska Högskolan. 2013 -- 2018
- Major in Machine Learning
- Exchange Year. Imperial College London. 2016 -- 2017
- Summer course in Chinese Culture and Language. Dalian University of Technology. 2014
- Economics I. Stockholms universitet. 2013
- Political Science I. Stockholms universitet. 2011
### Languages
| Language | Level |
| -------- | ------ |
| Swedish | Native |
| English | C2 |
|
