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diff --git a/content/cv.md b/content/cv.md index 52286d5..0dd520c 100644 --- a/content/cv.md +++ b/content/cv.md @@ -125,64 +125,63 @@ tbc... ### 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 |