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authorGustaf Rydholm <gustaf.rydholm@gmail.com>2022-08-20 01:20:24 +0200
committerGustaf Rydholm <gustaf.rydholm@gmail.com>2022-08-20 01:20:24 +0200
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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