NoSQL Experts in Munich

• 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

In my role as Senior Cloud DevOps Architect for BP, an oil and gas company, I had the mission to migrate the Electric Vehicle Charging platform of the EV Division from on-premises and Azure to AWS cloud, resulting in a hybrid multi-cloud, multi-tenant SaaS solution.

Deployment with Kubernetes for the application layer meant provisioning Kubernetes clusters managed by EKS and AKS, with a focus on integrating them into a multi-tenant environment. This integration was achieved by using Kubernetes namespaces and access controls to ensure data isolation and privacy enforcement.

In the database layer, we chose an RDS instance with PostgreSQL to support the backend infrastructure of our applications. Tenants shared the same RDS instance, but each had a dedicated schema.

To ingest near real-time data from physical charge points (CPOs), as IoT devices, via the OCPI protocol, we ran into significant delays with batch processing. As a result, we built a real-time streaming data pipeline using Apache Kafka, while prioritizing an event-driven architecture.

Led collaboration across multiple internal teams, external vendors, cloud providers, and on-site partners to integrate over five systems into a unified solution.

Achievements:

• Successfully designed and implemented hybrid multi-cloud solutions, integrating multiple cloud platforms (AWS, Azure) with on-premises infrastructure, using Site-to-Site VPNs, Firewalls, and Load Balancing.
• Led the migration of on-premises infrastructure to multi-cloud, multi-tenant infrastructure, resulting in 30% faster processing times.
• Migrated workloads from VMware and Hyper-V environments to cloud-based VMs, leveraging cloud-native services to optimize performance, cost efficiency, and scalability.
• Designed a multi-tenant Kubernetes platform leveraging the Kubernetes ecosystem, using Karpenter for dynamic EC2 node provisioning, KEDA for event-driven pod autoscaling (e.g., Kafka message lag), and Rancher for centralized monitoring of multiple clusters (EKS, AKS, or on-prem K8s), replacing Microsoft-centric Azure Arc management service.
• Designed and implemented Python-based FastAPI microservices as part of the EV core-backend on AWS EKS application layer, powering data ingestion and customer analytics pipelines.
• Developed asynchronous, event-driven APIs (Python-FastAPI) for real-time integration with CPOs, supporting OCPI 2.3 and OICP protocols.
• Designed and implemented a secure, production-grade Azure Databricks platform using Terraform, ensuring scalability and cost efficiency.
• Migrated on-premises ERP to a hybrid Dynamics 365 architecture with ERP hosted locally and CRM running in Azure, integrated via Azure Arc.
• Automated CI/CD pipelines for Databricks notebooks and jobs using GitHub Actions & Databricks CLI, reducing deployment time. Reduced infrastructure provisioning time by 70% by automating cloud resource deployment with GitOps.
• Ensured compliance with internal audit and data governance standards (GDPR) through OAuth2/OIDC-based authentication and fine-grained role-based access controls.
• Developed a Zero Trust security model, enforcing least-privilege access and microsegmentation, enhancing security posture and compliance with GDPR and NIST.
• Built interactive analytics dashboards in Amazon QuickSight, integrating data from S3 and Redshift to deliver real-time business insights and visualizations with embedded access for multi-tenant users.
• Led cloud security assessments and full-lifecycle cybersecurity integration during M&A, covering AWS, Azure, IAM (Entra ID), and data protection, while aligning security posture with NIST, ISO 27001, and GDPR across hybrid and cloud-native environments.
• Reduced cloud costs by 64% for a client's dev environment by implementing automated start/stop schedules for EC2 and RDS instances via AWS CDK with EventBridge Scheduler or AWS Systems Manager.

Tech stack:

• Infrastructure as Code: Terraform, AWS CDK, Ansible.
• Containers: Kubernetes on EKS, AKS, Docker.
• Streaming Data Processing: Kafka to Confluent Cloud, after AWS MSK.
• Frontend: TypeScript, React, NextJS, Hooks, Styled Components.
• Backend: Python with FastAPI, also Node.js with NestJS.
• Database: Aurora on PostgreSQL with TypeORM, RDS on SQL Server, Azure Databricks full setup and administration, ETL Pipelines.
• CI/CD and GitOps: GitHub Actions, Azure DevOps, ArgoCD.
• Monitoring and Observability: Prometheus and Grafana.
• Virtualization: Hyper-V, VMware Cloud on AWS, Azure Migrate.
• ERP Systems: Odoo, Microsoft Dynamics 365 Business Central on Azure, integrated with Azure Arc.
• Networking: Site-to-Site VPNs, AWS Direct Connect, Azure ExpressRoute, Firewalls (AWS Network Firewall, Azure Firewall).
• Security: IAM, NIST Framework, Zero Trust Security, AWS WAF, AWS Shield, GuardDuty.

• Build and operate an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for the automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, Autoformer).
• Implement CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform), data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) through training and evaluation to model registry and endpoint deployment.
• Integrate MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
• Develop and containerize PyTorch training jobs (Azure Notebook, Jupyter Notebooks) for price and time series forecasting (PFC models) with automatic rollout via Azure ML Endpoints and REST/gRPC interfaces, Docker containerization, secured with OAuth 2.0.
• Set up monitoring and alerting mechanisms (Prometheus, MLflow Metrics), log centralization, and cost monitoring.
• Automate infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connect to existing market data systems and event pipelines.
• Migrate existing workloads and databases (IONOS → Azure, MongoDB) with integration into central MLOps workflows and internal networks.
• Extend the platform with LLM-based tools (LangChain, LangServe) to integrate GPT-based analysis modules into existing Spring Boot services for market anomaly detection and automated reports.
• Analyze and architect a software solution to process large volumes of data efficiently (>3000 messages/sec.) (market data store).
• Spring Boot / Java 21 container development with RabbitMQ for distributing stock market data via MongoDB (Kubernetes) with fast storage of data in Redis RMaps, deduplication, forwarding messages to Read Model queues, and building Read Models for UI display in MongoDB.
• Integration of RESTHeart to create a REST API for MongoDB.
• Build an Angular frontend to simplify data queries and master data maintenance.
• Agentic coding with remote and local LLMs (Claude Sonnet, Ollama Qwen) and MCP servers.
• Develop Python scripts for transforming and cleaning incoming stock market data (Pandas, scikit-learn).

• Partner directly with C-level leadership (CEO, CAIO, CTO) on architecture, OKR strategy, and cross-team roadmap prioritization, translating strategic goals into structured engineering requirements.
• Surfaced and mapped technical debt across the entire organization with C-level leadership and co-defined a prioritized remediation strategy, balancing debt paydown against feature delivery.
• Led code reviews and technical standards across the team, fostering a mentor-first environment with two-way feedback dialogue — pairing on complex pipeline work and unblocking junior engineers on async architecture patterns.
• Re-architected the AI companion's core processing pipeline from synchronous to asynchronous with a queue-based worker architecture, enabling horizontal scalability and cutting upload processing time ~4x (from ~22s to 5–10s) while improving response accuracy.
• Designed an AI-driven document intelligence workflow with automatic multi-document classification, per-document summarization, and relevance guardrails for the patient care journey.
• Built a unified patient memory system (short- and long-term context) bridging the document vault and chatbot into a single bidirectional, context-aware platform.

• Building an independent enterprise IT setup — cloud strategy, network, AWS landing zone, security requirements, contract negotiations.
• Migration of all applications; avoiding high contractual penalties for the client.
• Onboarding and coordination o...

• Integrated data from various sources into the Datawarehouse using Microsoft SQL Server, ensuring data integrity and optimizing performance
• Developed and implemented multiple data visualization solutions in PowerBI, Excel, and Looker Studio, focusing on scalability and efficiency
• Managed close stakeholder relationships, ensuring consistent communication and understanding of data needs
• Conducted thorough requirement analysis to align with business objectives
• Led cross-functional teams to deliver data-driven projects on time

As the Solution Architect for Agentic AI in auto claims processing, I led global customer delivery implementations, encompassing solution design and detailing, multi-tenancy, process flows, integration with third-party solutions, and localization requirements.

• Architectural Analysis: Conducted in-depth analysis of business requirements, managing requirements and creating detailed specifications.
• Service Definition: Developed comprehensive technical definitions for services and integration contracts.
• AI Process Management: Automated AI process management, focusing on analysis, optimization, and continuous improvement.
• Requirements Gathering: Facilitated requirement-gathering sessions and analyzed business processes to identify optimization opportunities.
• Agile Collaboration: Employed agile methodologies, working closely with stakeholders to ensure alignment and responsiveness.
• Technical Support: Assisted senior management with technical analyses and deliverability assessments.

• Designed, built, and maintained scalable ETL/ELT data pipelines using Python, SQL, REST APIs, AWS Lambda, S3, PostgreSQL RDS, EventBridge, CloudWatch, Docker, Apache Airflow, and BigQuery – integrating data from GA4, Google Ads, Meta Ads, CMS, CRM, newsletters, events, and B2C ordering systems into analytics-ready datasets.
• Built a cross-brand lakehouse architecture from AWS to BigQuery – transforming raw JSON/CSV data into structured, partitioned, and reusable reporting layers with staging, intermediate, canonical, and mart models.
• Designed relational and dimensional data models: 3NF staging models, star schemas, fact tables, dimension tables, daily KPI aggregates, and dashboard-optimized marts for marketing, content, subscription, event, CRM, and revenue analysis.
• Implemented production-grade data quality and pipeline reliability features: incremental loads, idempotent upserts, deduplication, schema validation, row matching, null checks, anomaly detection, freshness monitoring, logging, retries, and error alerts.
• Automated cross-brand reporting processes and data products – pipelines for 73 newsletter campaigns, 31 lead list syncs, 52 event partner reports, and a 500K-record company matching pipeline; reduced manual data preparation by approx. 70% and increased analyst productivity by approx. 30%.

• Designed and built from scratch the automation framework using Playwright, Typescript, JavaScript, Gherkin, Cucumber, BDD, Maven, IntelliJ IDEA
• Defined, developed and maintained test plans, test scripts, reports and other QA documentation in Confluence
• Created and maintained test data with SQL Server Management Studio, Toad and Faker.js
• Performed web services and API testing using Swagger Open API, SoapUI, JSON, HTML and XML
• Executed functional, smoke, regression, black box, GUI, cross-browser, UAT and E2E testing using CrossBrowserTesting and Safari Web Inspector
• Integrated and maintained automated scripts within sprints following Kanban, CI/CD, GitHub, Docker, Azure Pipelines, Agile, Scrum, Kafka and Kibana
• Raised and managed defects in Azure DevOps
• Conducted mobile testing with Robot Framework and Appium

• Concept creation and implementing AI Agents in AWS Cloud
• Continuously alignment with stakeholders
• Collaborate with DevOps
• Technologies: Git, CI/CD (GitHub Actions), Python/ML, Streamlit, Deno/typescript, AWS SAM, AWS Bedrock, AWS Lambda, AWS Dynamo DB, AWS S3, AWS Event Bridge etc.

• Participated in the gradual modernization of components into cloud-native 12-factor applications.
• Worked closely with the business operations team to eliminate manual processes and resolve several performance bottlenecks.
• Designed and implemented a CI/CD pipeline to increase developer productivity, enforce quality and security checks, and automate product delivery.
• Migrated several components into the OpenShift Kubernetes cluster.
• Built a monitoring stack from scratch with Prometheus and Grafana to monitor services running in OpenShift.
• Developed dashboards in both Grafana and Splunk for operational transparency.
• Implemented an OIDC/OAuth2-based single sign-on (SSO) solution with Keycloak to secure multiple applications.
• Technologies: Java, Spring, Quarkus, Kafka, MySQL, Cassandra, Redis, Spring Data, Hibernate, Docker, Kubernetes, OpenShift, Keycloak, OIDC, OAuth2, Helm, Prometheus, Grafana, Splunk, Spark.

• Participated in the Riyadh Public Transport (KAPT) project introducing public transportation in Riyadh, supporting multimodal journeys combining bus, metro, car on demand.
• Worked via Thiqah and partnered with Andersen to refactor the mobile MaaS application DARB into a new microservices architecture under SCRUM framework, successfully completing Phase 1.
• Facilitated a team of 40 developers across Android, iOS, Web, Java, DevOps, architecture, analysis, and QA.
• Moderated Scrum events and communicated with the client, proactively reporting on project deadlines, scope, and challenges.
• Coordinated cross-team efforts between Thiqah specialists and GPS development teams, managing Jira and Azure DevOps trackers.
• Supported and improved Scrum processes throughout the project.
• Due to Thiqah’s takeover by Elm, subsequent phases were handled entirely in Saudi Arabia with GPS providing IT consulting.
• Tools: Azure DevOps, Jira, Confluence, Draw.IO, ChatGPT

• Backend development of B2B e-commerce solution using SAP Commerce Cloud, leveraging Java and Spring Framework with creation of REST APIs for effective communication.
• Resolved software defects, developed automated cron jobs and triggers, and authored unit tests with JUnit to uphold code stability and quality standards.
• Utilized Git-based version control systems (Bitbucket/GitHub) for source code management and used Jenkins to support continuous integration and development (CI/CD).
• Executed and closed multiple user stories and tickets across iterative bi-weekly sprints under Agile (SCRUM) methodology.
• Led incident resolution in the production environment and minimized downtime through real-time monitoring (Solr, SAP CCv2, OpenSearch Dashboards) and sustaining SLA targets above 99%.

• Designed and developed the "Credit Risk Engine" using Camunda, Node.js and Typescript
• Greenfield project for credit card credit assessment for existing and new customers, including EBA KPIs, SCHUFA and CRIF scorings
• Built and modeled workflows (BPMN) and decision logic (DMN) with Camunda Modeler in close collaboration with stakeholders
• Implemented service tasks, user tasks and jobs with Nest.js, Node.js and Typescript, including exception handling
• Backend-for-Frontend (BFF), frontend with React, Tailwind and Ant Design UI library
• CI/CD with GitLab, Kubernetes/Rancher