Google Cloud Run Experts in Germany

• Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
• Implementation of automated end-to-end cloud processes
• Development of LLM and NLP models
• Creation of interactive reports
• Support for national and international large corporations as well as medium-sized companies in implementing ML projects

• Acquires new B2B customers for an AI SaaS startup in the public tendering space and books product demos with IT decision-makers.
• Qualifies target customers based on a defined ideal customer profile, including discovery, needs analysis, and objection handling.
• Builds domain knowledge in public procurement (EVB-IT, German and EU tender portals) for technical discussions at eye level.

• Delivered MVP-focused full-stack architecture for a health-sector client: Vite/React frontend, backend services on Google Cloud Run, and Supabase for database plus IAM/authentication.
• Supported product requirements engineering and prioritized cost-aware workload placement, implementing browser-side/edge computation where feasible before moving logic to backend services.

• 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

• Analysis of the existing reporting platform including processes and governance topics with stakeholders from sales and marketing.

• Detailed analysis and evaluation of client-defined requirements for existing reporting and new dashboards.

• Supporting stakeholders in managing sales processes and early detection of KPI trends.

• Use of Microsoft Power BI as central analysis and reporting platform.

• Developing a proposal for the necessary evolution of processes and the Power BI platform.

• Gathering current business processes and defining company-wide KPIs in coordination with stakeholders.

• Analysis and inventory of the client's Power BI platform.

• Analysis of processes and data governance.

• Recording and documenting current business processes.

• Developing recommendations for process and reporting platform improvements.

• Designing and implementing dashboards in Power BI.

• Defining company-wide KPIs and aligning them with stakeholders.

• Microsoft Power BI.

• Data analytics.

• KPI definition and reporting.

• Dashboard design and data visualization.

• Stakeholder management and requirements management.

• Designed and deployed enterprise Retrieval-Augmented Generation (RAG) applications using LangChain, LangGraph, vector databases, embeddings, and open-source LLMs served through vLLM on GCP GPU infrastructure.
• Built agentic AI workflows using LangGraph with planning, reasoning, tool execution, persistent memory, session management, and Human-in-the-Loop approval mechanisms.
• Developed LLM-powered automation systems integrating BigQuery, SQL pipelines, and external advertising APIs including Meta, TikTok, Amazon, Snapchat, Google, and Pinterest, reducing manual operational workflows.
• Architected multi-agent AI systems for enterprise analytics and decision-support workflows, enabling autonomous task execution and intelligent data interactions.
• Implemented retrieval optimization strategies including multi-retriever architectures, semantic search, context optimization, and query improvement techniques, improving response relevance by approximately 40%.
• Engineered structured prompting strategies, function-calling schemas, and validation workflows to improve reliability of multi-step LLM applications.
• Designed scalable AI services using Python, FastAPI, Cloud Run, Pub/Sub, BigQuery, Docker, and cloud-native deployment architectures.

• Designed and implemented a Python-based ETL pipeline with the Dagster framework to transform raw energy data from heterogeneous sources using InfluxDB and visualizations in Grafana
• Defined time-based and dependency-based jobs
• Deployed to managed Kubernetes clusters using Helm
• Integrated InfluxDB Cloud
• Prepared data for use in Grafana, including cleaning, normalization, and time-based resampling in Python
• Developed dashboards and visualizations in Grafana
• Developed unit tests with mocking using pytest
• Set up a CI/CD pipeline in GitLab

Technologies: Python, Dagster, InfluxDB, Grafana, pandas, pytest, REST, CI/CD, GitLab, Container, Kubernetes, Helm, Docker, Cloud

• 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

• Feasibility study for quick implementation of requirements with AI-based development (vibe coding)
• Development of a single-page web app in Angular 20
• Development of a backend server application in Kotlin
• Integration with Google Cloud Platform (Firebase): authentication, Firestore NoSQL database, storage, Cloud Functions, hosting and Cloud Run
• Integration with a NEON PostgreSQL database
• Automated AI-based analysis of Donald Trump's posts on Truth Social and analysis of relevance for stock markets and geopolitical topics
• CI/CD via GitHub Actions using Docker and Google Cloud Run
• Technical environment: Angular 20 (Angular Material, RxJS), Kotlin 2.2.20, TypeScript 5.9.3, Spring Boot 3.5.6, Google Cloud Platform (Firebase, Cloud Run, Gemini, Vertex AI), ChatGPT Codex, Git, GitHub, SourceTree, IntelliJ WebStorm, IntelliJ IDEA

(Technology)

• Design and implementation of an SAP BTP CAP microservice to consolidate cross-system transactional checks between SAP Sales Cloud (C4C) and On-Premise SAP CRM (05/2024– 06/2025)
• Bi-directional Cloud-to-On-Premise integration enabling real-time validation of Contact lifecycle and open document dependencies prior to record updates (05/2024– 06/2025)

Responsibilities / Deliverables

• development of custom CAP (Cloud Application Programming) services on SAP BTP
• API design, data mapping, and end-to-end integration across C4C (Cloud Runway) and legacy CRM (Traditional Runway)

• 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.

• Mentored participants through structured learning paths and hands-on projects.
• Provided feedback and guidance on AI integration and product management best practices.

• Design and implementation of DevOps and CI/CD practices for data and analytics teams
• Introduction of infrastructure as code with Terraform (IaC)
• Setup and maintenance of GCP user and permission management with Terraform in multi-project environment
• Design and implementation of CI/CD pipelines with GitHub
• Leading and training developer team for the introduction of IaC and CI/CD practices
• Building and optimising database connectors with Apache Arrow for terabyte scale data extraction (Oracle, SAP)
• Optimising data lake storage and warehouse ingest

• 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