\section{Experience}
\vspace{-0.2cm}
% According to Google Recruiters, use the XYZ formula - Accomplished [X] as measured by [Y], by doing [Z]

\company{Software Engineer}
{Nexure/Electrolux AB}
{Stockholm, Sweden}
{Aug 2021 -- present}
{
Designed and maintained backend services for Appliance Repair Service Platforms using functional programming principles in Kotlin, supporting Sweden/NA launches and EU expansion.
\vspace{0.25em}
\begin{itemize}[topsep=0pt,parsep=0pt,partopsep=0pt,leftmargin=10pt,labelwidth=6pt,labelsep=4pt]
  \item \textbf{Functional Architecture}: Implemented immutable data models and \textbf{railway-oriented} error handling in Kotlin, mirroring Haskell Either monad and patterns.
  \item Event-Driven Systems: Built audit trails and transaction synchronization scripts for insurance processing (bordereau), handling 10K+ daily events – directly applicable to event sourcing.
  \item Cloud-Native Infrastructure: Deployed Kubernetes services on EKS using Helm/Terraform, managing stateful PostgreSQL workloads and AWS data services (SQS/SNS).
  \item Data Integrity: Optimized complex SQL queries for PostgreSQL and resolved cross-database inconsistencies.
  \item Security and Auth: Engineered JWT validation libraries and permission APIs, securing microservices with Auth0.
  \item \textbf{Tech Stack}: Kotlin (functional paradigms) \textbullet\ PostgreSQL \textbullet\ Kubernetes/EKS \textbullet\ AWS \textbullet\ Terraform \textbullet\ Event-Driven Architecture \textbullet\ Auth0 \textbullet\ Datadog
\end{itemize}
}

% \company{Software Engineer}
% {Nexure/Electrolux AB}
% {Stockholm, Sweden}
% {Aug 2021 -- present}
% {
% Designed and maintained backend services for Appliance Repair Service Platforms using functional programming principles in Kotlin, supporting Sweden/NA launches and EU expansion.
% \vspace{0.25em}
% \begin{itemize}[topsep=0pt,parsep=0pt,partopsep=0pt,leftmargin=10pt,labelwidth=6pt,labelsep=4pt]
%   \item Developed \textbf{subscriptions engine} and \textbf{user management service} using \textbf{Kotlin} and \textbf{Ktor}, enabling features like recurring payments and user account management.
%   \item Contributed to \textbf{Auth0 migration} of users from legacy to new platform, ensuring seamless transitions without user intervention.
%   \item Developed \textbf{synchronization scripts} to update \textbf{APIs}, roles, and permissions in \textbf{Auth0}, ensuring consistency across dev, staging, and prod environments.
%   \item Introduced \textbf{type library} for shared business logic across \textbf{microservices}, enhancing code consistency and maintainability.
%   \item Built \textbf{JWT deserialization} library and \textbf{APIs} for permission-based checks on \textbf{HTTP} and \textbf{WebSocket} endpoints, securing all public-facing services.
%   \item Introduced \textbf{railway-oriented} and \textbf{workflow-oriented programming}, improving error handling and process clarity in \textbf{microservices}.
%   \item Supported two in-house platform launches in Sweden and North America for \textbf{Appliance Repair Service} features, with ongoing EU expansion.
%   \item Contributed to \textbf{infrastructure} via \textbf{Terraform} for \textbf{AWS} resources (RDS, SNS, SQS).
%   \item Enhanced legacy platform with \textbf{Appliance Repair Service} features, including bordereau for insurance transactions, 
%     tax calculation updates, and cross-database record corrections, improving system reliability.
%   \item Tech stack: \textbf{Kotlin} (Ktor), \textbf{Kubernetes}, \textbf{PostgreSQL}, \textbf{MongoDB}, \textbf{Helm}, \textbf{Terraform}, \textbf{AWS}, \textbf{CI/CD}, \textbf{WebSocket}, \textbf{Auth0}, \textbf{DataDog}.
% \end{itemize}
% }

\company{Machine Learning Engineer (Cybersecurity)}
{Saab AB}
{Stockholm, Sweden}
{Aug 2020 -- Aug 2021}
{
\begin{itemize}[topsep=0pt,parsep=0pt,partopsep=0pt,leftmargin=10pt,labelwidth=6pt,labelsep=4pt]
  \item \textbf{Event-Driven Architecture}: Architected \textbf{Kafka-driven microservices} for sensor data ingestion and processing, replacing batch-based database fetching and improving system responsiveness.
  \item \textbf{Automated Model Deployment}: Developed modular Python pipelines for training and evaluating deep learning models, enabling \textbf{automated selection} of optimal models to production. This system streamlined the transition from research to deployment.
  \item \textbf{Cloud-Native Optimization}: Reduced Docker image sizes from 2GB to 73MB using multistage builds and Alpine Linux. This improved CI/CD pipeline efficiency, slashing average build times from 10–30 minutes to under 10 seconds with \textbf{Tekton}.
  \item \textbf{Distributed Systems}: Engineered a \textbf{graph-based sensor fusion microservice} to correlate data across sensors, enhancing complex pattern analysis for anomaly/threat detection.
  \item \textbf{DevOps Leadership}: Spearheaded development tooling practices, deploying and maintaining a private PyPI server to enforce versioning and resolve dependency conflicts, improving team productivity.
  \item \textbf{Tech Stack}: Python, Kafka, Kubernetes, PostgreSQL, Redis, TensorFlow, Tekton, Docker.
\end{itemize}
}

\company{Machine Learning Engineer (Surveillance)}
{Saab AB}
{Stockholm, Sweden}
{Aug 2018 -- Aug 2020}
{
\begin{itemize}[topsep=0pt,parsep=0pt,partopsep=0pt,leftmargin=10pt,labelwidth=6pt,labelsep=4pt]
 \item \textbf{Signal Processing}: Built simulation software for realistic radar/communication signal environments, enabling training and evaluating machine learning models for electronic warfare applications.
 \item \textbf{Algorithm Research}: Researched and prototyped multiple stages of the radar warning receiver with machine learning models, focusing on improving signal classification (communication vs radar) and source grouping.
 \item \textbf{Tech stack}: Python, VHDL, Signal Processing.
\end{itemize}
}

% \company{Machine Learning Engineer}
% {Saab AB}
% {Stockholm, Sweden}
% {Aug 2018 -- Aug 2021}
% {
% Developed machine learning solutions for defense systems, creating \textbf{data pipelines}, \textbf{machine learning models}, \textbf{microservices}, and \textbf{CI/CD workflows} for cybersecurity and electronic warfare applications.
% \vspace{0.25em}
% \begin{itemize}[topsep=0pt,parsep=0pt,partopsep=0pt,leftmargin=10pt,labelwidth=6pt,labelsep=4pt]
%   \item Built \textbf{Python}-based \textbf{data pipelines}, automating dataset preprocessing for machine learning.
%   \item Architected \textbf{asynchronous microservices} with \textbf{Kafka}, enabling continuous sensor data subscription and processing across services, improving system responsiveness over batch-based database fetching.
%   \item Designed modular \textbf{PyTorch pipelines} for \textbf{deep learning} model training and evaluation, automating best-model selection for production deployment of trajectory prediction models.
%   \item Implemented trajectory prediction models with \textbf{LSTM} and \textbf{transformer} based architectures for anomaly detection.
%   \item Deployed a private \textbf{PyPI server}, enforcing versioning and resolving dependency conflicts, improving development experience for the entire team.
%   \item Developed a \textbf{graph-based} \textbf{sensor fusion} microservice, enhancing complex pattern analysis for cybersecurity applications.
%   \item Optimized \textbf{Docker images} from 2GB to 73MB using \textbf{multistage builds} and Alpine, reducing CI/CD build times from 10--30 minutes to under 10 seconds with \textbf{Tekton}.
%   \item Researched replacing different stages of the radar warning receiver with machine learning models, enhancing classification of communication vs radar signals, improving detection of Low Probability of Intercept radar pulses. 
%   \item Tech stack: \textbf{Python} (Pandas, PyTorch, FastAPI), \textbf{Kafka}, \textbf{Redis}, \textbf{PostgreSQL}, \textbf{ArangoDB}, \textbf{Kubernetes}, \textbf{Helm}, \textbf{Docker}, \textbf{Tekton}, \textbf{VHDL}.
% \end{itemize}
% }


%\company{Master's Thesis}
%{Ericsson AB}
%{Jan 2018 -- June 2018 \qquad 6 months}
%{Stockholm, Sweden}
%{
%Built a \textbf{natural language processing} system using a biologically %inspired machine learning algorithm, called \textbf{Hierarchical Temporal %Memory}, for detecting anomalies in system logs. Demonstrated that the system %was able to achieve similar performance to the existing system. Developed with %\textbf{Python}, \textbf{NuPIC}, and \textbf{pandas}.
%}\\

%\ruler
