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%	HEADER
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\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{{ML Engineer $\vert$ Production AI in Semiconductor Manufacturing $\vert$ Python, GenAI, Kubernetes}}


\begin{document}

\vspace{-0.15cm}

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%	SUMMARY
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\begin{rSection}{Summary}
ML and data engineer with 7+ years applying \textbf{Python}, \textbf{Java}, and \textbf{production ML deployment} across semiconductor manufacturing, applied research, and telecom. At Bosch Semiconductor, containerized ML inference (Docker, Kubernetes, Ansible) for automated defect classification in a 24/7 300mm fab. Contributed ML and NLP components to Fraunhofer CML's ARTUS speech recognition research. At Swisscom, apply \textbf{generative AI} and custom GPTs to automate development and engineering workflows alongside production data pipelines. M.Eng.\ (thesis grade 1.0) applying neural networks, PSO, and fuzzy logic. Motivated to bring ML engineering and semiconductor domain knowledge to AI-based verification research. German native, fluent English.
\end{rSection}
\vspace{-0.15cm}

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%	TECHNICAL SKILLS — Format C, 5 groups (4-3-2-2-2 = 13 lines)
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\begin{rSection}{Technical Skills}

\begin{skillgroup}{Machine Learning \& AI}
\skilldash{\textbf{ML inference deployment}, MLOps, \textbf{generative AI / LLMs}, custom GPT development, automated defect detection}
\skilldash{\textbf{NLP}, speech recognition, neural networks, fuzzy logic, particle swarm optimization (PSO), pattern recognition}
\skilldash{PyTorch, Scikit-learn, TensorFlow/Keras (IBM cert), Pandas, NumPy, Matplotlib, Apache Spark ML}
\skilldash{Computer vision (wafer defect classification), time-series analysis, statistical modeling, quantitative ML}
\end{skillgroup}

\begin{skillgroup}{Programming Languages \& Tools}
\skilldash{\textbf{Python} (expert), \textbf{Java} (strong), C++, C\#, JavaScript, SQL (Oracle, Impala, Teradata, Postgres)}
\skilldash{PySpark, \textbf{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}

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%	PROFESSIONAL EXPERIENCE
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\begin{rSection}{Professional Experience}

% --- Swisscom (Oct 2023 -- Present) — 4 bullets: SW-3, SW-1, SW-2, SW-5 ---
\begin{rSubsection}{GenAI-Driven Engineering, Cloud Data Infrastructure \& 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 ETL pipelines (Oracle, Kafka to Teradata DWH in \textbf{Python}) as Component Owner, ensuring data availability, SLA compliance, and Data Governance across business-critical production data flows.
\item Applied \textbf{generative AI} and custom GPTs with domain-specific knowledge bases to automate development and engineering workflows, reducing manual effort in code review, documentation, and data pipeline troubleshooting.
\end{rSubsection}

% --- Bosch (Feb 2020 -- Dec 2022) — 4 bullets: BS-1, BS-2, BS-4, BS-3 ---
\begin{rSubsection}{ML Inference Deployment \& Semiconductor Manufacturing Analytics}{\textcolor{black!60}{Feb 2020 -- Dec 2022}}{(Senior) Data \& ML Engineer, Robert Bosch Semiconductor Manufacturing}{Dresden, Germany}
\item Containerized \textbf{ML inference} (\textbf{Docker}, \textbf{Kubernetes}, Ansible) for a 24/7 semiconductor fab, automating image-based defect classification and replacing manual wafer inspection across active 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 Application Owner 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, applying speech recognition and machine learning in a safety-critical 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 the CI/CD pipeline, shortening feedback loops and improving release-over-release reliability.
\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 XLDeploy and contributed to an Apache Camel / Spring Boot dispatcher integration PoC.
\end{rSubsection}


\end{rSection}
\vspace{-0.15cm}

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%	EDUCATION — FIXED
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\begin{rSection}{Education}
{M.Eng.\ Computer Aided Engineering (Software Design \& Engineering)} \hfill {\textcolor{black!60}{Oct 2010 -- Jul 2013}}\\
{Universität der Bundeswehr München}; 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}}\\
{Universität der Bundeswehr München}, Munich, Germany
\end{rSection}
\vspace{-0.15cm}

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%	CERTIFICATIONS & AWARDS
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\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}

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\textit{Languages: German (native), English (fluent)}
\end{center}

\end{document}
