\documentclass[11pt,a4paper,roman]{moderncv}
\usepackage[english]{babel}
\moderncvstyle{classic}
\moderncvcolor{green}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{lmodern}
\usepackage{ragged2e}
\usepackage[scale=0.79]{geometry}
\usepackage[version=4,arrows=pgf-filled]{mhchem}
\renewcommand*{\makeletterclosing}{\par\vspace{2ex}\closingname\par}

% ========== CONTACT ==========
\name{Dennis}{Thiessen}
\address{Bern, Switzerland}
\phone[mobile]{+41 795 955 585}
\email{dennis@thiessen.io}
% =============================

\begin{document}

\recipient{To}{Kraken (Payward)\\AI Infrastructure Team, Data Organization\\Remote --- Switzerland}
\date{\today}
\opening{Dear AI Infrastructure Team,}
\makelettertitle

\begin{justify}
When Kraken shipped its open-source CLI for AI agents in March, with a built-in MCP server that lets Claude Code and Cursor execute directly against live markets, it confirmed a bet I had been making as an engineer and Kraken customer since 2017: agentic systems only matter once someone makes them reliable in production. That is the work of the AI Infrastructure team, and the reason I am writing about the Site Reliability Engineer, AI Agents role.

For nearly three years at Swisscom, Switzerland's largest telco, I have built and operated Python services on Kubernetes with GitLab CI/CD, owning them from build through production rollout and on-call. Before that I migrated our legacy Teradata and Oracle ETL to a cloud-native AWS stack (S3, Glue, Athena with Iceberg, Airflow) provisioned as code. My IaC is CloudFormation rather than Terraform, but the model is identical and I would close that gap fast. I also own security and access controls for the Data Lake team as its Security Champion.

What fits me most here is turning experimental systems into production ones. At Bosch I containerized and orchestrated ML inference (Docker, Kubernetes, Ansible) into a 24/7 semiconductor fab with no maintenance windows, and built the observability stack (ELK, Grafana, Prometheus, Loki) around it. At Swisscom I now build the self-service, governed data products that downstream teams and agentic AI workflows query. Reliability, model serving, observability, and a platform other teams consume as a service are the problems your posting names.

Doing this for a crypto-native company is the part I would be most invested in. I hold BTC and ETH, write Solidity in my own time, and have watched Kraken operate through fifteen years of market cycles. I would welcome the chance to talk about keeping your agent infrastructure reliable as it scales.
\end{justify}

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

\end{document}
