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- Nexure AB.
- Software Engineer. Aug 2021 -- Present
- - bla bla
+ - 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.
- I develop and maintain backend web services (microservices) in the
- payments/subscription domain. From time to time I also do some
- infrastructure work in k8s and AWS with terraform.
+ *Keywords: Microservices, Kubernetes, Infrastructure, Helm, CI/CD, Kotlin,
+ Spring, AWS, Terraform*
- Saab AB.
- Machine Learning Engineer. Aug 2018 -- Aug 2021
@@ -155,8 +160,8 @@ tbc...
- 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.
- - Improved the docker image size of microservices from ~2 GB to ~73 MB
- by utilizing multistage builds and alpine base images.
+ - Improved 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
@@ -166,7 +171,18 @@ tbc...
Helm, CI/CD*
- Radar Warning Receiver. Aug 2018 -- Aug 2020
- - bla bla
+ - 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, and where it would be possible computationally and
+ data availability.
+ - Held in several presentations of machine learning papers in a company
+ reading group.
+
+ *Keywords: Deep Learning, Signal Processing*
### Institutions