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\section{Experience}
\company{Software Engineer}
{Nexure/Electrolux AB}
{Stockholm, Sweden}
{Aug 2021 -- present}
{
Backend engineer on Appliance Repair Service platforms (Sweden, North America, EU)
within a 100+ microservice ecosystem. Primary contributor to 10+ services, introducing
functional programming patterns to Kotlin codebases.
\vspace{0.25em}
\begin{itemize}[topsep=0pt,parsep=0pt,partopsep=0pt,leftmargin=10pt,labelwidth=6pt,labelsep=4pt]
\item \textbf{Functional Programming:} Applied railway-oriented error handling,
immutable data models, and shared type libraries across services, bringing Haskell's
Either monad patterns to Kotlin. Emphasized type safety and composability in API design.
\item \textbf{Service Development:} Built subscription, payment, and user management
microservices with Kotlin/Ktor. Developed RESTful and WebSocket APIs with asynchronous
event processing via SQS/SNS for non-blocking workflows.
\item \textbf{Security \& Access Control:} Engineered JWT validation library for Auth0
and permission enforcement APIs with resource-level access control, verifying user
permissions and resource ownership across services.
\item \textbf{Infrastructure:} Configured Kubernetes resources (deployments, services,
secrets, ConfigMaps) and contributed Terraform resources (SNS, SQS, ingresses) to
shared infrastructure codebase.
\item \textbf{Tech Stack}: Kotlin (Ktor) \textbullet\ PostgreSQL \textbullet\
Kubernetes/EKS \textbullet\ AWS \textbullet\ Terraform \textbullet\ Auth0
\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 Kafka-based microservices for
sensor data ingestion and processing, replacing batch-based database polling. Enabled
multiple services to subscribe to incoming data streams for parallel processing and
pattern analysis.
\item \textbf{Sensor Fusion System:} Developed graph-based microservice for correlating
data across heterogeneous sensor systems, enabling downstream services to perform
complex pattern analysis and anomaly detection.
\item \textbf{ML Pipeline Infrastructure:} Built modular Python pipelines for training
and evaluating deep learning models with automated metric-based model selection for
production deployment.
\item \textbf{DevOps \& Infrastructure:} Redesigned Docker build strategy across all
Python microservices, reducing image sizes from ~2GB to ~73MB using multistage builds
and Alpine base images. This eliminated persistent storage issues in self-hosted Docker
registry and reduced CI/CD build times from 10-30 minutes to seconds in Tekton.
\item \textbf{Developer Tooling:} Deployed and maintained private PyPI server for <10
developers, eliminating circular dependencies and enforcing proper package versioning.
Significantly improved code quality and development workflow across the team.
\item \textbf{Tech Stack}: Python \textbullet\ Kafka \textbullet\ Kubernetes
\textbullet\ PostgreSQL \textbullet\ Redis \textbullet\ Tekton \textbullet\ 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 Built signal simulation software for radar and communication environments, enabling
team to train and evaluate ML models. Researched ML approaches for signal classification
under compute and data constraints.
\item \textbf{Tech Stack}: Python \textbullet\ Signal Processing
\end{itemize}
}\\
|
