Google Cloud Storage Experts in Germany

Remote freelance role focused on Agentic AI strategy, secure application patterns, and reusable agentic workflows for a government agency.

• Development and implementation of an open source Agentic AI strategy for a government agency, with a focus on GDPR, security, and self hosted solutions
• Development of reusable agentic workflows and mini applications that enable non technical employees to solve business problems independently
• Implementation of internal business applications with Single Sign On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
• Implementation of nine mini applications with Single Sign On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
• Techstack: Python, Nextjs, Typescript, Streamlit, Anthropic SDK (Claude), Azure, Linux Ubuntu, PostgreSQL, MS SQL, Angular, Authentik

Embedded Analytics & AI-assisted BI

Design and development of an integrated analytics solution based on ASP.NET Core, Power BI Embedded, and LLM services to provide context-based business information.

Development of an AI agent with Function/Tool Calling for the secure orchestration of REST APIs, SQL data sources, and technical services within defined business processes.

Building automated BI workflows including workspace management, deployment processes, and scheduled refresh via the Power BI REST API.

Implementation of secure service-to-service communication with Microsoft Entra ID and Service Principal, and integration into existing enterprise system landscapes.

Technologies: ASP.NET Core, C#/.NET, Power BI Embedded, Power BI REST API, LLM API, AI Agents, Function/Tool Calling, Entra ID

• Mentor team of 6 ML platform engineers through weekly 1:1s, technical design reviews, and best practices, improving team velocity by 35% through structured sprint planning and skill development programs
• Define 2025–2026 ML platform roadmap in collaboration with Data Science, Cloud Engineering, and Product teams, prioritizing automated model governance, cost attribution systems, and multi-environment deployment strategies
• Partner with Data Science, SRE, and Product stakeholders to align ML platform capabilities with business objectives, reducing data scientist deployment friction by 60% through self-service platforms
• Architect and deliver production-grade MLOps platform supporting 50+ models in production with automated promotion pipelines, versioning, and rollback capabilities, achieving 99.5% platform uptime SLA
• Design distributed ML pipeline architecture using Metaflow and Argo Workflows (Vertex Pipelines-compatible), reducing model training time by 30% and deployment cycles from 2 weeks to 3 days through full CI/CD automation
• Build containerized ML services on Kubernetes with auto-scaling policies, resource quotas, and multi-tenancy isolation, optimizing infrastructure costs by $180K annually (25% reduction)
• Implement monitoring, alerting, and performance tracking using Prometheus, Grafana, and custom instrumentation, reducing model debugging time by 50% and establishing model performance SLOs
• Lead development of RAG-based document intelligence platform using LangChain, LangGraph, and vector databases, implementing agentic AI workflows for automated financial document processing
• Implement Infrastructure-as-Code using Terraform for reproducible environment provisioning and GitOps workflows, reducing infrastructure drift incidents by 80%
• Design role-based access control for ML platform, implement model lineage tracking, and establish audit trails for regulatory compliance aligned with enterprise IAM best practices

• It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
• GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
• Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
• CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment

• Implementing applications with Kotlin and Ktor as microservices
• Using MongoDB in the MongoDB Atlas cloud
• Asynchronous communication of services via Google Pub/Sub
• Using Kotest and MockK for unit tests
• Developing an administration frontend with TypeScript, React and Express.js
• Provisioning environments in GCP using Terraform
• Implementing CI/CD processes with GitHub Actions
• Operating scalable production and test environments in GCP with Kubernetes, Helm and Flux CD
• Monitoring environments with Prometheus and Grafana
• Providing BI data in the Google BigQuery data warehouse

• Cloud Computing Fundamentals & Architecture: Expertise in core cloud concepts, including the three main Service Models (IaaS, PaaS, SaaS) and diverse Deployment Models (Public, Private, Hybrid, Multi-cloud).
• Modern Application Deployment Strategies (GCP Focus): Instruction on the GCP Application Hosting Spectrum, covering Virtual Machines, Containers (Kubernetes and Cloud Run), Platform as a Service (App Engine), and Serverless Computing (Functions as a Service - FaaS).
• Data Management & Big Data Analytics: Comprehensive coverage of Cloud Storage options (Object, Block, File) and Database solutions, including Relational (Cloud SQL), NoSQL (Firestore, BigTable, Memorystore), and serverless enterprise data warehousing (BigQuery).
• DevOps and Infrastructure Automation: Skills in DevOps principles, including Continuous Integration (CI), Continuous Delivery (CD), Infrastructure as Code (IaC) using tools like Terraform, and implementing effective Monitoring and Logging for system observability.
• Emerging Technologies & Responsible Cloud Use: Focus on crucial topics like Cloud and IoT Security, Identity and Access Management (IAM), data privacy, and the ethical considerations of cloud and massive data collection.

• Architected ETL services for event-driven data exchange between enterprise systems on a Kafka streaming backbone.
• Optimized CI/CD pipelines for Azure AKS deployments and improved OpenSearch monitoring and alerting for proactive incident detection.

Tech: Java / Kotlin, Quarkus, Kafka / Avro, Azure / AKS, Azure Storage Container, ArgoCD, GitLab CI, OpenSearch, Terraform

• A warehouse valuation system was reimplemented using Java, Spring Boot, and Camunda. The backend solution focuses on integration and batch calculations, the frontend on managing formulas and reviewing results.
• Java 21
• Spring Boot
• JPA
• Maven
• REST
• Kafka
• PostgreSQL
• DB2
• Liquibase
• Google Cloud Storage
• Keycloak
• GitLab CI/CD
• Helm
• Terragrunt
• SonarQube
• Angular
• IntelliJ
• JUnit 5
• Mockito
• Open API

• IAUP (Interregional Academy of Personnel Management) commissioned the development of a distance learning system for its secondary school subdivision.
• Development of the website [link]
• Development of a billing system and integration with various payment systems
• Development of a distance learning system based on the open source Moodle solution
• Integration of the distance learning system into the user office on the [link] website
• Front-end: Vue.js v2, Nuxt.js, JavaScript, HTML, CSS, SCSS
• Back-end: LAMP (Linux, Apache, MySQL, PHP), PHP Framework Laravel, Redis, Swagger, MySQL 8.x, PayPal API
• DevOps: Docker, CI/CD, GIT

• Mobile: Kotlin Multiplatform (KMP), Kotlin, Swift, SwiftUI, Jetpack Compose, Ktor, Room, Koin
• Backend: Spring Boot, GCP Cloud Run, GCP Load Balancer, GCP Eventarc
• Planned and developed a cloud backend architecture with Google Cloud Platform
• Developed a cross-platform architecture with Kotlin Multiplatform (KMP)
• Tech lead for comprehensive large project including webshop, two apps and omnichannel backend

• Analysis and assessment of government business processes with regard to migration capability
• Definition and preparation of the technical framework conditions in the new master data center
• Development and optimization of migration procedures and processes
• Transformation of existing solutions to new technical standards (technology refresh)
• Coordination of architecture and technical cross-cutting topics

• Fine-tuned a quantized LLaMA model with LoRA, optimizing hyperparameters for domain-specific, large-scale NLP applications.
• Led development of LLM-based hybrid RAG architectures using the LangChain framework for the legal domain, integrating Document Extraction, Vector Search, Speech-to-Text processing, and Prompt Engineering methods using OpenAI APIs.
• Built an LLM-powered translation service combining OpenAI Whisper for transcription with domain-specific translation and prompting to handle sensitive diplomacy terminology.
• Developed and integrated REST APIs with FastAPI and Pydantic for AI models, collaborating with front-end teams to deploy production-ready applications in secure cloud environments.
• Automated LLM workflows with CI/CD pipelines, containerized models using Docker, and deployed to AWS for scalable cloud infrastructure.

• Fullstack development of our product: a web app (VueJS), Chrome extension (VueJS), SDK (JS), API (NodeJS), GraphQL server (Hasura, Postgres)
• Included development of multiple user-facing features, conversion of app and extension from Vue2 to Vue3, implementation of E2E testing system (Playwright), as well as debugging and refactors, on a team from 2 to 4 developers
• Deployment on Google Cloud Storage, including management of CI pipelines and jobs