diff options
Diffstat (limited to 'content/cv.md')
| -rw-r--r-- | content/cv.md | 89 |
1 files changed, 44 insertions, 45 deletions
diff --git a/content/cv.md b/content/cv.md index bf01d14..812cbf8 100644 --- a/content/cv.md +++ b/content/cv.md @@ -134,51 +134,50 @@ tbc... *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* +- Machine Learning Engineer (Cyber Security). Saab AB. 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* + +- Machine Learning Engineer (Surveillance). Saab AB. 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 |