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\name{Dennis Thiessen, M.Eng.}
\address{\href{https://linkedin.com/in/dennis-thiessen}{LinkedIn}}
\address{dennis@thiessen.io \\ +41 795 955 585}
\address{Bern, Switzerland $\vert$ German citizen $\vert$ Available remote across DACH/EU/UK}
\address{{AI Infrastructure Engineer $\vert$ Model Inference $\cdot$ MLOps $\cdot$ Observability $\vert$ K8s $\cdot$ AWS $\cdot$ Python}}


\begin{document}

\vspace{-0.15cm}

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%	SUMMARY
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\begin{rSection}{Summary}
Software engineer with 11+ years building production data and AI infrastructure --- containerized \textbf{ML inference} into a 24/7 Bosch semiconductor fab (\textbf{Docker}, \textbf{Kubernetes}, Ansible), and currently own Switzerland's largest telco's cloud-native data platform on \textbf{AWS} (\textbf{Airflow}, Kafka, PySpark, GitLab CI/CD). Built \textbf{custom GPTs} and \textbf{LiteLLM}-routed agent assistants to automate engineering workflows. Earlier engineered distributed real-time backends at Vizrt for CNN, BBC, Al Jazeera. \textbf{Python} expert; AWS Solutions Architect; \textbf{Solidity} smart-contract developer (personal projects); long-time Kraken customer.
\end{rSection}
\vspace{-0.15cm}

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%	TECHNICAL SKILLS — Format C, 5 groups
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\begin{rSection}{Technical Skills}

\begin{skillgroup}{AI / ML Infrastructure \& Agentic Workflows}
\skilldash{\textbf{ML inference}, \textbf{model serving}, \textbf{MLOps}, model deployment, evaluation frameworks, computer vision, NLP}
\skilldash{\textbf{Custom GPTs}, \textbf{LiteLLM} (LLM API gateway), \textbf{Kiro} / spec-driven dev, GitHub Copilot, prompt engineering}
\skilldash{\textbf{PyTorch}, Scikit-learn, TensorFlow/Keras, Spark ML, deep learning, time-series analysis, anomaly detection}
\skilldash{Speech recognition, image classification, defect detection, predictive maintenance, multi-modal data processing}
\skilldash{ML dataset curation, data quality validation, model performance monitoring, observability for ML systems}
\end{skillgroup}

\begin{skillgroup}{Distributed Systems \& Data Engineering}
\skilldash{\textbf{Kafka}, \textbf{Airflow}, \textbf{PySpark} / Apache Spark, Apache Iceberg, Hadoop / ImpalaSQL, Databricks}
\skilldash{\textbf{AWS} (S3, Glue, Athena/Iceberg, Redshift, Lambda, \textbf{Airflow}, CloudFormation), Teradata, OracleDB}
\skilldash{ETL/ELT pipeline design, data modeling, data governance, SLA / on-call ownership, batch and stream processing}
\skilldash{High-throughput data pipelines, real-time event processing, data lakehouse, distributed batch, data lineage}
\end{skillgroup}

\begin{skillgroup}{Cloud-Native Infrastructure \& Observability}
\skilldash{\textbf{Kubernetes}, \textbf{Docker}, Ansible, GitLab CI/CD, Jenkins, Infrastructure as Code, DevSecOps}
\skilldash{ELK Stack (Elasticsearch, Logstash, Kibana), \textbf{Grafana}, \textbf{Prometheus}, Loki, log aggregation, alerting}
\skilldash{AWS Lambda, CloudWatch, ECR, ECS, Step Functions, SQS, SNS, event-driven architectures, serverless}
\end{skillgroup}

\begin{skillgroup}{Programming Languages \& Tools}
\skilldash{\textbf{Python} (expert), \textbf{Java} (strong), SQL, JavaScript, Bash, Git, .NET / Entity Framework, FastAPI}
\skilldash{Pandas, NumPy, SQLAlchemy, pytest, Jupyter Notebooks, dbt, code review, Agile/Scrum, software design patterns}
\skilldash{C++ (Vizrt 2017--18, legacy), C\# (Bosch / Fraunhofer 2018--22, legacy), Express.js, shell scripting}
\end{skillgroup}

\begin{skillgroup}{Crypto / Web3 \& Certifications}
\skilldash{\textbf{Solidity} (Ethereum smart contracts, personal projects), blockchain / DeFi, Kraken (long-term user since 2017)}
\skilldash{AWS Certified Solutions Architect -- Associate (active until Sep 2027), 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) — 5 bullets: SW-2, SW-1, SW-GenAI, SW-3, SW-6 ---
\begin{rSubsection}{AI/ML Infrastructure, Agentic Workflows \& Cloud-Native Pipelines}{\textcolor{black!60}{Oct 2023 -- Present}}{Staff Data, Analytics \& AI Engineer, Swisscom (Schweiz) AG}{Bern, Switzerland}
\item Owned Fulfillment and Product Analysis ETL pipelines (Oracle, \textbf{Kafka} to Teradata in \textbf{Python}) as Component Owner; enforced data governance and SLA compliance for business-critical telecom-scale production flows.
\item Migrated legacy Teradata/Oracle ETL stack to \textbf{AWS} cloud-native (S3, Glue, \textbf{Airflow}, Athena/Iceberg, Redshift, CloudFormation IaC), enabling serverless data processing for ML and analytics workloads.
\item Built \textbf{custom GPTs} and \textbf{LiteLLM}-routed \textbf{agent assistants} (LLM API gateway, model routing) to automate Swisscom engineering workflows (code review, documentation, pipeline triage) on a spec-driven \textbf{Kiro} toolchain.
\item Deployed and operate \textbf{Python} data services on \textbf{Kubernetes} with GitLab CI/CD automation, owning containerized delivery from build and test to production rollout in an agile DevOps team across multiple data products.
\item Applied \textbf{PySpark} and distributed computing within the Swisscom Data Lake platform, extending \textbf{Python} pipeline capabilities to large-scale batch workloads for Fulfillment and Product Analysis data.
\item Drove \textbf{Python} process automation and 3rd-level root cause analysis across recurring data workflows under on-call SLA; delivered reliable data products to downstream \textbf{ML} and analytics consumers.
\end{rSubsection}

% --- Bosch (Feb 2020 -- Dec 2022) — 4 bullets: BS-1, BS-4, BS-3, BS-2 ---
\begin{rSubsection}{Production ML Inference \& Observability in 24/7 Semiconductor Manufacturing}{\textcolor{black!60}{Feb 2020 -- Dec 2022}}{(Senior) Data \& ML Engineer, Robert Bosch Semiconductor Manufacturing}{Dresden, Germany}
\item Designed \textbf{ML inference} infrastructure (\textbf{Docker}, \textbf{Kubernetes}, Ansible) for Bosch's 24/7 semiconductor fab, automating image-based defect classification across 300mm wafer production lines without downtime.
\item Built anomaly detection PoC: ELK Stack with \textbf{Kafka} (\textbf{Docker}), \textbf{Grafana}, \textbf{Prometheus} and Loki monitoring, providing centralized observability for 24/7 semiconductor manufacturing infrastructure.
\item Served as Application Owner for the semiconductor analytics suite and upstream data pipelines, defining SLOs, managing vendors, and delivering user training and documentation across fab operations teams.
\item Built data services in \textbf{Python}, Java and C\# over OracleDB and Hadoop/ImpalaSQL, supplying semiconductor analysis teams with structured access to defect management and process optimization data.
\end{rSubsection}

% --- Fraunhofer (Sep 2018 -- Oct 2019) — 3 bullets: FC-2, FC-1, FC-3 ---
\begin{rSubsection}{Applied NLP/ML Research \& Microservice 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 transcription combining speech recognition and machine learning in 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 across logistics stakeholders, ports, operators and research partners.
\end{rSubsection}

% --- Vizrt (Jul 2017 -- May 2018) — 2 bullets: VZ-1 (Python-led), VZ-2 ---
\begin{rSubsection}{Distributed Real-Time Backend Engineering at Broadcast Scale}{\textcolor{black!60}{Jul 2017 -- May 2018}}{DevOps Engineer, Vizrt}{Bergen, Norway}
\item Built distributed real-time video transcoding backend components in \textbf{Python} (with legacy C++ modules) for Vizrt's broadcast platform, serving global media customers including 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 release quality.
\end{rSubsection}

% --- Generali (May 2015 -- Jun 2017) — 2 bullets: GN-1, GN-3 ---
\begin{rSubsection}{Test Automation, BDD Ownership \& Java Backend}{\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 teams and presented the methodology to the Java Community.
\item Pioneered UIPath RPA at Generali GDIS, developing PoCs and serving as internal RPA contact for Generali group companies; extended automation from test tooling into business process automation.
\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}

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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}}\\
{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}
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%	CERTIFICATIONS & AWARDS — FIXED
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\begin{rSection2}{Certifications \& Awards}
\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{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{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}
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\textit{Languages: German (native), English (fluent)}
\end{center}

\end{document}
