170 lines
12 KiB
TeX
170 lines
12 KiB
TeX
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%----------------------------------------------------------------------------------------
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% HEADER
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%----------------------------------------------------------------------------------------
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\name{Dennis Thiessen, M.Eng.}
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\address{\href{https://linkedin.com/in/dennis-thiessen}{LinkedIn}}
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\address{dennis@thiessen.io \\ +41 795 955 585}
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\address{Bern, Switzerland $\vert$ German citizen $\vert$ Available remote across DACH/EU/UK}
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\address{{AI Infrastructure Engineer $\vert$ Model Inference $\cdot$ MLOps $\cdot$ Observability $\vert$ K8s $\cdot$ AWS $\cdot$ Python}}
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\begin{document}
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\vspace{-0.15cm}
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%----------------------------------------------------------------------------------------
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% SUMMARY
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%----------------------------------------------------------------------------------------
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\begin{rSection}{Summary}
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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.
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\end{rSection}
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\vspace{-0.15cm}
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%----------------------------------------------------------------------------------------
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% TECHNICAL SKILLS — Format C, 5 groups
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%----------------------------------------------------------------------------------------
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\begin{rSection}{Technical Skills}
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\begin{skillgroup}{AI / ML Infrastructure \& Agentic Workflows}
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\skilldash{\textbf{ML inference}, \textbf{model serving}, \textbf{MLOps}, model deployment, evaluation frameworks, computer vision, NLP}
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\skilldash{\textbf{Custom GPTs}, \textbf{LiteLLM} (LLM API gateway), \textbf{Kiro} / spec-driven dev, GitHub Copilot, prompt engineering}
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\skilldash{\textbf{PyTorch}, Scikit-learn, TensorFlow/Keras, Spark ML, deep learning, time-series analysis, anomaly detection}
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\skilldash{Speech recognition, image classification, defect detection, predictive maintenance, multi-modal data processing}
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\skilldash{ML dataset curation, data quality validation, model performance monitoring, observability for ML systems}
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\end{skillgroup}
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\begin{skillgroup}{Distributed Systems \& Data Engineering}
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\skilldash{\textbf{Kafka}, \textbf{Airflow}, \textbf{PySpark} / Apache Spark, Apache Iceberg, Hadoop / ImpalaSQL, Databricks}
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\skilldash{\textbf{AWS} (S3, Glue, Athena/Iceberg, Redshift, Lambda, \textbf{Airflow}, CloudFormation), Teradata, OracleDB}
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\skilldash{ETL/ELT pipeline design, data modeling, data governance, SLA / on-call ownership, batch and stream processing}
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\skilldash{High-throughput data pipelines, real-time event processing, data lakehouse, distributed batch, data lineage}
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\end{skillgroup}
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\begin{skillgroup}{Cloud-Native Infrastructure \& Observability}
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\skilldash{\textbf{Kubernetes}, \textbf{Docker}, Ansible, GitLab CI/CD, Jenkins, Infrastructure as Code, DevSecOps}
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\skilldash{ELK Stack (Elasticsearch, Logstash, Kibana), \textbf{Grafana}, \textbf{Prometheus}, Loki, log aggregation, alerting}
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\skilldash{AWS Lambda, CloudWatch, ECR, ECS, Step Functions, SQS, SNS, event-driven architectures, serverless}
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\end{skillgroup}
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\begin{skillgroup}{Programming Languages \& Tools}
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\skilldash{\textbf{Python} (expert), \textbf{Java} (strong), SQL, JavaScript, Bash, Git, .NET / Entity Framework, FastAPI}
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\skilldash{Pandas, NumPy, SQLAlchemy, pytest, Jupyter Notebooks, dbt, code review, Agile/Scrum, software design patterns}
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\skilldash{C++ (Vizrt 2017--18, legacy), C\# (Bosch / Fraunhofer 2018--22, legacy), Express.js, shell scripting}
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\end{skillgroup}
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\begin{skillgroup}{Crypto / Web3 \& Certifications}
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\skilldash{\textbf{Solidity} (Ethereum smart contracts, personal projects), blockchain / DeFi, Kraken (long-term user since 2017)}
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\skilldash{AWS Certified Solutions Architect -- Associate (active until Sep 2027), Data Engineering with AWS (Udacity, 2026)}
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\skilldash{IBM AI Engineering Specialization, AI for Trading Nanodegree (Udacity, 2021), iSAQB CPSA-F (2016)}
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\end{skillgroup}
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\end{rSection}
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\vspace{-0.15cm}
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%----------------------------------------------------------------------------------------
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% PROFESSIONAL EXPERIENCE
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%----------------------------------------------------------------------------------------
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\begin{rSection}{Professional Experience}
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% --- Swisscom (Oct 2023 -- Present) — 5 bullets: SW-2, SW-1, SW-GenAI, SW-3, SW-6 ---
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\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}
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\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.
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\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.
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\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.
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\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.
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\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.
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\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.
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\end{rSubsection}
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% --- Bosch (Feb 2020 -- Dec 2022) — 4 bullets: BS-1, BS-4, BS-3, BS-2 ---
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\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}
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\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.
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\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.
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\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.
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\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.
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\end{rSubsection}
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% --- Fraunhofer (Sep 2018 -- Oct 2019) — 3 bullets: FC-2, FC-1, FC-3 ---
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\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}
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\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.
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\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).
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\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.
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\end{rSubsection}
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% --- Vizrt (Jul 2017 -- May 2018) — 2 bullets: VZ-1 (Python-led), VZ-2 ---
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\begin{rSubsection}{Distributed Real-Time Backend Engineering at Broadcast Scale}{\textcolor{black!60}{Jul 2017 -- May 2018}}{DevOps Engineer, Vizrt}{Bergen, Norway}
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\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.
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\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.
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\end{rSubsection}
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% --- Generali (May 2015 -- Jun 2017) — 2 bullets: GN-1, GN-3 ---
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\begin{rSubsection}{Test Automation, BDD Ownership \& Java Backend}{\textcolor{black!60}{May 2015 -- Jun 2017}}{IT Consultant, Generali Deutschland Informatik Services}{Hamburg, Germany}
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\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.
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\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.
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\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.
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\end{rSubsection}
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\end{rSection}
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\vspace{-0.15cm}
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%----------------------------------------------------------------------------------------
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% EDUCATION — FIXED
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%----------------------------------------------------------------------------------------
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\begin{rSection}{Education}
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{M.Eng.\ Computer Aided Engineering (Software Design \& Engineering)} \hfill {\textcolor{black!60}{Oct 2010 -- Jul 2013}}\\
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{Universit\"at der Bundeswehr M\"unchen}; thesis at Tongji University, Shanghai \hfill Thesis Grade: \textbf{1.0}\\
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{\small Thesis: \textit{Development of a Web-Based Remote Fault Diagnosis System} (Neural Networks, PSO, Fuzzy Logic)}
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{B.Eng.\ Information and Telecommunication Technologies} \hfill {\textcolor{black!60}{Oct 2007 -- Sep 2010}}\\
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{Universit\"at der Bundeswehr M\"unchen}, Munich, Germany
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\end{rSection}
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\vspace{-0.15cm}
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%----------------------------------------------------------------------------------------
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% CERTIFICATIONS & AWARDS — FIXED
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%----------------------------------------------------------------------------------------
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\begin{rSection2}{Certifications \& Awards}
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\item \textbf{AWS Certified Solutions Architect -- Associate}, Amazon Web Services (2024, active until Sep 2027).
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\item \textbf{Data Engineering with AWS Nanodegree}, Udacity (2026). AWS data pipeline architecture.
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\item \textbf{IBM AI Engineering Specialization}, Coursera. Deep learning, TensorFlow, Keras, Apache Spark ML.
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\item \textbf{AI for Trading Nanodegree}, Udacity / WorldQuant (2021). Quantitative ML, time-series analysis.
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\item \textbf{iSAQB CPSA -- Foundation Level}, iSAQB (2016). Certified Professional for Software Architecture.
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\item \textbf{ITIL Foundation Certificate in IT Service Management}, PEOPLECERT / AXELOS (2016).
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\end{rSection2}
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\begin{center}
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\vspace{0.1cm}
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\textit{Languages: German (native), English (fluent)}
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\end{center}
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\end{document}
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