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\name{Dennis}{Thiessen, M.Eng.}
\address{Bern, Switzerland}{}{}
\phone[mobile]{+41~795~955~585}
\email{dennis@thiessen.io}
\extrainfo{\href{https://linkedin.com/in/dennis-thiessen}{linkedin.com/in/dennis-thiessen}}

\begin{document}

\recipient{Hiring Committee}{Joint Warfare Centre\\Stavanger, Norway}
\date{10 July 2026}
\opening{Dear Members of the Hiring Committee,}
\makelettertitle

\begin{justify}
JWC's AI in Audacious Training work is moving AI from concept into practical support for exercise teams: scenario content is being built, tested, reviewed and refined with operators. I am applying for the Staff Officer (2030 Digitalisation -- Artificial Intelligence Engineer) role because this is the work I want to do: build dependable AI-enabled capabilities that improve operational workflows while keeping the people who use the output involved in delivery and review. That emphasis on expert judgement matches how I approach applied AI.

At Swisscom, I configure domain-grounded LLM agents in a Swisscom-owned web interface, choosing available models and supplying knowledge bases for Q\&A, migration assistance and data mapping. The role also includes governed data products and metadata on AWS, plus Python services on Kubernetes with GitLab CI/CD. As Component Owner for business-critical Fulfillment pipelines, I work with data quality, governance, incident response and on-call responsibility. That work makes the link between governed inputs and useful AI output concrete. This is the operating discipline I would bring to JWC's data-centric exercise systems.

Previously at Bosch Semiconductor, I designed and implemented Docker, Kubernetes and Ansible integration for automated image-based defect classification in a 24/7 fab. There was no useful separation between an ML model and its operating environment: data access, delivery, monitoring and user trust all mattered. At Fraunhofer, I also contributed ML and NLP components to ARTUS, a research project for automatic transcription of sea-rescue communications. Together, those experiences give me practical ML delivery and applied language-AI context.

My six years as a German Armed Forces officer, ending as a Second Lieutenant, provide a genuine connection to the structured, multinational setting of the JWC. As a German national, I meet the nationality condition for this post. I would welcome the opportunity to discuss how my production ML, data engineering and careful LLM-application experience can support JWC's digital transformation and its operators.
\end{justify}

\vspace{0.3cm}
{Sincerely,\\
Dennis Thiessen, M.Eng.\\
Staff Data, Analytics \& AI Engineer\\
Swisscom (Schweiz) AG}

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