162 lines
11 KiB
TeX
162 lines
11 KiB
TeX
\documentclass{resume}
|
|
\usepackage{hyperref}
|
|
\usepackage{enumitem}
|
|
\usepackage{fontawesome}
|
|
\usepackage{tikz}
|
|
\usepackage{graphicx}
|
|
\hypersetup{
|
|
colorlinks = true,
|
|
linkcolor = [rgb]{0.9,0.4,0.4},
|
|
anchorcolor = [rgb]{0.9,0.4,0.4},
|
|
citecolor = [rgb]{0.4,0.4,0.4},
|
|
filecolor = [rgb]{0.4,0.4,0.4},
|
|
urlcolor = [rgb]{0.0,0.0,0.99},
|
|
}
|
|
\usepackage{xcolor}
|
|
\usepackage[utf8]{inputenc}
|
|
\usepackage[T1]{fontenc}
|
|
\usepackage{lmodern}
|
|
\usepackage[version=4,arrows=pgf-filled]{mhchem}
|
|
\usepackage[includefoot,left=0.5in,top=0.5in,right=0.5in,bottom=0.2in,textwidth=7.5in,textheight=10.8in]{geometry}
|
|
\usepackage{fancyhdr}
|
|
\pagestyle{fancy}
|
|
\fancyhf{}
|
|
\renewcommand{\headrulewidth}{0pt}
|
|
\fancyfoot[R]{\hfill \thepage/\pageref{LastPage}}
|
|
\newcommand{\tab}[1]{\hspace{.2667\textwidth}\rlap{#1}}
|
|
\newcommand{\itab}[1]{\hspace{0em}\rlap{#1}}
|
|
|
|
%----------------------------------------------------------------------------------------
|
|
% HEADER
|
|
%----------------------------------------------------------------------------------------
|
|
\name{Dennis Thiessen, M.Eng.}
|
|
\address{\href{https://linkedin.com/in/dennis-thiessen}{LinkedIn}}
|
|
\address{dennis@thiessen.io \\ +49 177 282 7302}
|
|
\address{Bern, Switzerland $\vert$ German citizen $\vert$ Open to relocation to Dresden}
|
|
\address{{AI Engineer $\vert$ Production ML $\cdot$ GenAI $\cdot$ Kubernetes $\vert$ Automotive Semiconductor}}
|
|
|
|
|
|
\begin{document}
|
|
|
|
\vspace{-0.15cm}
|
|
|
|
%----------------------------------------------------------------------------------------
|
|
% SUMMARY
|
|
%----------------------------------------------------------------------------------------
|
|
\begin{rSection}{Summary}
|
|
ML and data engineer with 7+ years deploying \textbf{Python}, \textbf{Docker/Kubernetes}, and \textbf{production ML} across automotive semiconductor and enterprise telecom. At Bosch in Dresden, deployed ML inference into a resource-constrained 24/7 fab for automated defect classification. At Swisscom, own AWS data pipelines with cross-functional stakeholders and apply \textbf{generative AI} and custom GPTs to automate workflows. Contributed ML/NLP to Fraunhofer's ARTUS speech recognition research. M.Eng.\ (thesis grade 1.0) in neural network-based fault diagnosis. German native, fluent English.
|
|
\end{rSection}
|
|
\vspace{-0.15cm}
|
|
|
|
%----------------------------------------------------------------------------------------
|
|
% TECHNICAL SKILLS — Format C, 5 groups (4-3-2-2-2 = 13 lines)
|
|
%----------------------------------------------------------------------------------------
|
|
\begin{rSection}{Technical Skills}
|
|
|
|
\begin{skillgroup}{Machine Learning \& AI}
|
|
\skilldash{\textbf{ML inference deployment}, MLOps, \textbf{generative AI / LLMs}, custom GPT development, \textbf{LangChain}}
|
|
\skilldash{\textbf{Deep learning}, NLP, speech recognition, neural networks, computer vision (wafer defect classification)}
|
|
\skilldash{\textbf{PyTorch}, Scikit-learn, \textbf{TensorFlow}/Keras (IBM cert), Pandas, NumPy, Matplotlib, Spark ML}
|
|
\skilldash{Anomaly detection, time-series analysis, statistical modeling, quantitative ML, pattern recognition}
|
|
\end{skillgroup}
|
|
|
|
\begin{skillgroup}{Programming Languages \& Tools}
|
|
\skilldash{\textbf{Python} (expert), \textbf{Java} (strong), C++, C\#, JavaScript, SQL (Oracle, Impala, Teradata, Postgres)}
|
|
\skilldash{PySpark, Bash, Flask/FastAPI, Express.js, .NET/Entity Framework, SQLAlchemy}
|
|
\skilldash{Git, pytest, Agile/Scrum, software architecture (iSAQB CPSA certified), technical documentation}
|
|
\end{skillgroup}
|
|
|
|
\begin{skillgroup}{Cloud \& Container Infrastructure}
|
|
\skilldash{\textbf{Docker}, \textbf{Kubernetes}, Ansible, AWS (S3, Glue, Athena/Iceberg, Redshift, Lambda, Airflow, CloudFormation)}
|
|
\skilldash{GitLab CI/CD, Jenkins, Infrastructure as Code, DevSecOps, build automation, CI/CD quality gates}
|
|
\end{skillgroup}
|
|
|
|
\begin{skillgroup}{Data Engineering \& Observability}
|
|
\skilldash{Apache Kafka, Hadoop/ImpalaSQL, OracleDB, Teradata DWH, ETL/ELT pipeline design, data modeling}
|
|
\skilldash{ELK Stack (Elasticsearch, Logstash, Kibana), Grafana, Prometheus, Loki, SQL performance tuning}
|
|
\end{skillgroup}
|
|
|
|
\begin{skillgroup}{Certifications}
|
|
\skilldash{AWS Certified Solutions Architect -- Associate (2024, active), Data Engineering with AWS (Udacity, 2026)}
|
|
\skilldash{IBM AI Engineering Specialization, AI for Trading Nanodegree (Udacity, 2021), iSAQB CPSA-F (2016)}
|
|
\end{skillgroup}
|
|
|
|
\end{rSection}
|
|
\vspace{-0.15cm}
|
|
|
|
%----------------------------------------------------------------------------------------
|
|
% PROFESSIONAL EXPERIENCE
|
|
%----------------------------------------------------------------------------------------
|
|
\begin{rSection}{Professional Experience}
|
|
|
|
% --- Swisscom (Oct 2023 -- Present) — 4 bullets: SW-3, SW-1, SW-2, SW-GenAI ---
|
|
\begin{rSubsection}{GenAI-Driven Engineering, Cloud Data Infrastructure \& ML Pipelines}{\textcolor{black!60}{Oct 2023 -- Present}}{Staff Data, Analytics \& AI Engineer, Swisscom (Schweiz) AG}{Bern, Switzerland}
|
|
\item Deployed and operated \textbf{Python} applications on \textbf{Kubernetes} with GitLab CI/CD, owning the full containerized delivery lifecycle from build and test automation to production rollout in an agile DevOps team.
|
|
\item Migrated legacy ETL pipelines to \textbf{AWS} (S3, Glue, Athena/Iceberg, Redshift, Airflow, CloudFormation), replacing Teradata/Oracle workflows with scalable, serverless cloud-native data processing.
|
|
\item Owned Fulfillment and Product Analysis ETL pipelines (Oracle, Kafka to Teradata DWH in \textbf{Python}) as technical project lead, coordinating cross-functional data governance and SLA compliance for production flows.
|
|
\item Applied \textbf{generative AI} and custom GPTs with domain-specific knowledge bases to automate code review, documentation, and pipeline troubleshooting, which cut manual effort across engineering workflows.
|
|
\end{rSubsection}
|
|
|
|
% --- Bosch (Feb 2020 -- Dec 2022) — 4 bullets: BS-1, BS-2, BS-4, BS-3 ---
|
|
\begin{rSubsection}{Production ML Deployment \& Automotive Semiconductor Analytics}{\textcolor{black!60}{Feb 2020 -- Dec 2022}}{(Senior) Data \& ML Engineer, Robert Bosch Semiconductor Manufacturing}{Dresden, Germany}
|
|
\item Deployed \textbf{ML inference} (\textbf{Docker}, \textbf{Kubernetes}, Ansible) into a resource-constrained 24/7 semiconductor fab, automating image-based defect classification and replacing manual inspection across 300mm production lines.
|
|
\item Built data services in \textbf{Python}, Java, and C\# over OracleDB and Hadoop/ImpalaSQL, supplying semiconductor analysis teams with on-demand access to defect management and process optimization data.
|
|
\item Delivered anomaly detection PoC using ELK Stack and Kafka (\textbf{Docker}) with Grafana/Prometheus/Loki monitoring, validating centralized alerting for 24/7 semiconductor manufacturing infrastructure.
|
|
\item Held technical project lead responsibility for semiconductor analytics platforms and data pipelines, defining SLOs, delivering training, and managing vendor and stakeholder relationships across the fab.
|
|
\end{rSubsection}
|
|
|
|
% --- Fraunhofer (Sep 2018 -- Oct 2019) — 3 bullets: FC-2, FC-1, FC-3 ---
|
|
\begin{rSubsection}{Applied ML/NLP Research \& Software Engineering}{\textcolor{black!60}{Sep 2018 -- Oct 2019}}{Research Software Engineer, Fraunhofer-Center for Maritime Logistics CML}{Hamburg, Germany}
|
|
\item Contributed \textbf{ML and NLP} components to ARTUS, a Fraunhofer research project for automatic sea rescue speech transcription that combined speech recognition and machine learning for a safety-critical maritime domain.
|
|
\item Set up Jenkins CI/CD pipeline with quality gates independently, introducing build automation to the research team; developed SCEDAS crew scheduling software (C\#, .NET, MS SQL Server, Entity Framework).
|
|
\item Built microservices (Express.js, \textbf{Docker}, SQLite) for MISSION, a Fraunhofer research platform for maritime data exchange between logistics stakeholders including ports, operators, and research partners.
|
|
\end{rSubsection}
|
|
|
|
% --- Vizrt (Jul 2017 -- May 2018) — 2 bullets: VZ-1, VZ-2 ---
|
|
\begin{rSubsection}{Python/C++ Backend Engineering \& CI/CD Automation}{\textcolor{black!60}{Jul 2017 -- May 2018}}{DevOps Engineer, Vizrt}{Bergen, Norway}
|
|
\item Engineered distributed video transcoding backend components in \textbf{Python} and C++ for Vizrt's broadcast platform, contributing to the core A/V processing pipeline used by CNN, BBC, and Al Jazeera.
|
|
\item Built automated integration and unit test suite for A/V streaming (\textbf{Python}) and integrated quality gates into CI/CD, which shortened the feedback loop for new features and raised overall release quality.
|
|
\end{rSubsection}
|
|
|
|
% --- Generali (May 2015 -- Jun 2017) — 2 bullets: GN-1, GN-3 ---
|
|
\begin{rSubsection}{Test Automation \& BDD Technical Ownership}{\textcolor{black!60}{May 2015 -- Jun 2017}}{IT Consultant, Generali Deutschland Informatik Services}{Hamburg, Germany}
|
|
\item Introduced BDD test automation to Generali (Serenity-BDD, Selenium, JBehave), running the initial PoC and taking technical ownership; trained project teams and presented the methodology across the Java Community.
|
|
\item Developed Java/J2EE application features for the PIA-Postkorb workflow portal; migrated WebServices to the XLDeploy process and contributed to an Apache Camel / Spring Boot dispatcher integration PoC.
|
|
\end{rSubsection}
|
|
|
|
|
|
\end{rSection}
|
|
\vspace{-0.15cm}
|
|
|
|
%----------------------------------------------------------------------------------------
|
|
% EDUCATION — FIXED
|
|
%----------------------------------------------------------------------------------------
|
|
\begin{rSection}{Education}
|
|
{M.Eng.\ Computer Aided Engineering (Software Design \& Engineering)} \hfill {\textcolor{black!60}{Oct 2010 -- Jul 2013}}\\
|
|
{Universit\"at der Bundeswehr M\"unchen}; thesis at Tongji University, Shanghai \hfill Thesis Grade: \textbf{1.0}\\
|
|
{\small Thesis: \textit{Development of a Web-Based Remote Fault Diagnosis System} (Neural Networks, PSO, Fuzzy Logic)}
|
|
|
|
{B.Eng.\ Information and Telecommunication Technologies} \hfill {\textcolor{black!60}{Oct 2007 -- Sep 2010}}\\
|
|
{Universit\"at der Bundeswehr M\"unchen}, Munich, Germany
|
|
\end{rSection}
|
|
\vspace{-0.15cm}
|
|
|
|
%----------------------------------------------------------------------------------------
|
|
% CERTIFICATIONS & AWARDS
|
|
%----------------------------------------------------------------------------------------
|
|
\begin{rSection2}{Certifications \& Awards}
|
|
\item \textbf{IBM AI Engineering Specialization}, Coursera. Deep learning, TensorFlow, Keras, Apache Spark ML.
|
|
\item \textbf{AI for Trading Nanodegree}, Udacity / WorldQuant (2021). Quantitative ML, time-series analysis.
|
|
\item \textbf{AWS Certified Solutions Architect -- Associate}, Amazon Web Services (2024, active until Sep 2027).
|
|
\item \textbf{Data Engineering with AWS Nanodegree}, Udacity (2026). AWS data pipeline architecture.
|
|
\item \textbf{iSAQB CPSA -- Foundation Level}, iSAQB (2016). Certified Professional for Software Architecture.
|
|
\item \textbf{ITIL Foundation Certificate in IT Service Management}, PEOPLECERT / AXELOS (2016).
|
|
\end{rSection2}
|
|
|
|
\begin{center}
|
|
\vspace{0.1cm}
|
|
\textit{Languages: German (native), English (fluent)}
|
|
\end{center}
|
|
|
|
\end{document}
|