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 ### Experience
 
-- Nexure AB
-  - Software Engineer. 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*
-
-- Saab AB
-  - Machine Learning Engineer. 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.
-        - 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
-        to improved caching capabilities.
-
-        *Keywords: Deep Learning, 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, 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*
+- 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