Load Balancing Experts in Germany

• Development of an agent-based governance platform for the automated assurance of EU AI Act compliance and ODA-compliant orchestration of AI services in complex enterprise environments.

• Design and implementation of an agent-based "Mission Control" framework (Aletheia Conductor) for autonomous state monitoring and process control.

• Development of "Compliance-as-Code" (CaC) solutions based on OPA/Rego for system-wide enforcement of regulatory guardrails.

• Integration of TM Forum ODA standards (TMF630, TMF622, TMF642) to ensure interoperability and standardization.

• Building a highly available event-driven architecture using Redpanda and CloudEvents v1.0 for near-real-time event processing.

• Implementation of an audit-proof "Evidence Chain" through cryptographic linking of trace logs in preparation for automated audits.

• Tech Stack: Java 21 (Quarkus Native), TypeScript (Next.js), Redpanda (Kafka API), CloudEvents v1.0, OPA (Open Policy Agent) & Rego, TimescaleDB, ZincSearch, Redis, TM Forum ODA, Git, GitHub, Clean Code Development, Like-C4.

Built an international e-commerce platform for a multi-vendor marketplace for digital assets from scratch. Designed and operated cloud native architectures at enterprise scale.

• Designed and operated a highly scalable microservice and serverless architecture
• Built the complete cloud infrastructure with Terraform + AWS CDK in AWS
• Provisioned ECS/EKS clusters (Fargate), Application Load Balancers (reverse proxy), and Lambda functions
• Observability & tracing with CloudWatch, DataDog, Prometheus, and Grafana
• End-to-end setup with DataDog (formerly AWS CloudWatch), Prometheus, and custom Grafana dashboards
• Integration of advanced metrics (including ORM mapper) and distributed tracing with Jaeger
• Robust backup and disaster recovery strategies
• RDS Postgres backups and hourly snapshots
• Read-only, asynchronously synchronized replicas with automated master failover in emergencies
• Minute-level rollback capability through versioned Docker images on ECS and Git-based CI/CD pipelines
• Created CI/CD pipelines with GitHub Actions for automated multi-stage deployments (Dev, Testing, Prod)
• Integrated Stripe for international payment processing
• Built a marketplace payment system with multiple parties and payout routines
• Used Algolia for high-performance real-time search of digital assets on the platform
• Federation of services with GraphQL and Hasura
• Later migration to GraphQL Mesh
• Test Driven Development (TDD) - unit, integration, and E2E testing with Jest, Vitest, and Playwright
• Used Next.js / React for modern frontend applications in the nx monorepo
• Enterprise security architecture & access control
• Integration of JWT tokens with Auth0, OAuth, OIDC, IP guards, BOLA protection, and secret vaults
• Authorization concepts with RBAC, ABAC, and native Postgres Row-Level Security (RLS)
• Built internal microfrontends with Retool for fast prototyping and operational business processes

Technologies: ABAC, AWS CDK, AWS CloudWatch, AWS ECS, AWS EKS, AWS Fargate, AWS RDS, AWS S3, Algolia, Auth0, DataDog, Docker, GitHub Actions, Grafana, GraphQL, GraphQL Mesh, Hasura, JWT, Jaeger, Java, JavaScript, Jest, Kotlin, Kubernetes, Monorepo, Next.js, OIDC, Playwright, Postgres, Postgres RLS, Prometheus, RBAC, Redis, Retool, Serverless, Stripe, Terraform, TypeScript, Vitest

• Operation and further development of the network and security infrastructure.

• Monitoring, analysis and resolution of network and security incidents.

• Cross-department collaboration with other specialist teams for operations, further development and reporting.

• Firewall vulnerability analysis.

• Firewall rule approvals.

• Troubleshooting IP communication issues in the network and firewall infrastructure.

• Security-critical IT infrastructure, processing of personal data, compliance with legal regulations.

• Products: Palo Alto Networks Firewalls, Cisco ACI, Checkpoint Firewalls, F5

• Technologies: SDN, SDWAN, Cisco EPIC, Cisco ACI

• Modernization of a large-scale Java enterprise application*

The project is modernizing a complex enterprise application that has grown over many years. The existing Spring-based legacy system runs on Java 8, OSGi, and Eclipse RCP and is being gradually migrated to a modern, maintainable architecture with Java 25 and Quarkus.

Marcus works on analysis, architecture, refactoring, and implementation. One focus is on untangling historically grown structures and dependencies and on building a clean, sustainable Java and Quarkus technology stack.

Tools & technologies: Java 8, Java 25, Quarkus, Hibernate ORM with Panache, EclipseLink, OSGi, Eclipse RCP, Maven, JUnit, Mockito, REST, JSON, Git, Eclipse IDE, IntelliJ IDEA Ultimate, Jira, Confluence

With project PRIZMA, DB will modernize its infrastructure on the one hand, and develop a fail-safe IT landscape on the other hand, which can be restored quickly and securely in case of a disaster.

• Capture current architectures of existing systems as well as methodical consulting and development of target architectures
• Deepen and maintain the building plan / target IT landscape
• Implement technical architecture concepts & architecture descriptions
• Implement migration concepts for updating and further developing the platform and information systems
• Assess submitted improvement suggestions as part of the project
• Capability management: identify capability gaps, develop target visions, and support transformation planning within the enterprise architecture.
• Create a compatibility matrix of the components in use and compare dependencies of specific versions
• Create an IT concept for extending the platform with the following topics: hardware and software requirements, security, licensing, high availability, load balancing, backup & recovery, update strategy, monitoring integration, etc.
• Coordinate with business architects as well as technical architects from the cross-functional architecture area of the PRISMA program for the topics (backup, Active Directory, monitoring, Citrix, and business applications ...)
• Status meetings and alignment of project planning with the Release Train Engineer / Project Manager
• Advise the Release Train Engineer / Project Manager in identifying project risks
• Advise the System Architect Engineers in steering the implementation of the concept
• Implement the IT concept
• Document the infrastructure

Label: MS Project, LINUX, Windows, ORACLE, Java, REST, SharePoint, Microsoft Exchange, UML, Enterprise Architect, BPMN, AZURE, AWS, V-MODEL, Micro Service, VisualStudio, SAP S/4HANA, SCRUM(SAFE), ESB (TIBCO), Python, Innovator, LeanIX (TOGAF), Ansible, Ansible Tower, Ansible Automation, ROBOT, SpringBoot

As project manager for the Odoo implementation at a small company, I was responsible for designing, implementing, and training a fully integrated CRM and accounting system. Through structured requirements analysis, precise data migration, and targeted change management, I was able to complete the rollout in just eight weeks. The project led to a significant reduction in manual tasks, faster business processes, and increased real-time transparency.

Main Responsibilities

Requirements analysis and process mapping Configuration of Odoo modules: CRM, Sales, and Accounting Data migration (Excel/CSV → Odoo) and quality control Creation of workflows, automated email rules, and dashboards Conducting training sessions and providing support after go-live Project coordination (budget, schedule, stakeholder communication)

Key Achievements

Full implementation of the Odoo suite within the set timeframe (8 weeks) 30% reduction in accounting time and 25% acceleration of the lead-to-sale flow 100% customer satisfaction after go-live, based on survey results Successful migration of 100% of existing master data without data loss Establishment of a sustainable support infrastructure (3-month post go-live support)

Impact

Improved decision-making through real-time dashboards and automated reports Increased efficiency and cost savings (≈ €8,000/month) Scalability for future growth (additional modules can be integrated seamlessly) Strengthened sales and finance departments through seamless process integration

This summary highlights how I created concrete and measurable value for the company through structured project work, technical expertise, and targeted training.

Task:

• Design and build a proof of concept (PoC) for a future-proof virtualization platform, taking secure system architectures into account
• Assess the current state of existing infrastructures and develop selection and evaluation criteria for the right OS virtualization platform
• Carry out the requirements analysis and then create and prioritize tickets in the ticket system
• Complete and continuously update a tool evaluation matrix based on PoC results
• Support team knowledge building through clear documentation of the approach and results in Confluence
• Enterprise analysis of existing hardware (creating different BoMs)

Technologies: Vmware, Vmware Aria Operations, Osism, Canonical OpenStack, FishOs, Linux, Terraform, Ansible, Confluence, Alma

Designed and developed an AI-powered automation platform using n8n to analyze social media niches, identify target audiences, and automate marketing strategy generation. The solution combined AI agents, workflow orchestration, and data analysis to automate research processes and generate data-driven insights.

• Designed and implemented complex automation workflows using n8n
• Developed AI-powered analysis agents for market and audience research
• Integrated multiple APIs and AI services into automated workflows
• Built automated market, competitor, and target audience analysis pipelines
• Leveraged Large Language Models (LLMs) for information summarization, classification, and prioritization
• Containerized and deployed the platform using Docker

Technologies: n8n, AI Agents, OpenAI APIs, Prompt Engineering, LLMs, Docker, Linux, REST APIs, Webhooks

• Led end-to-end delivery of complex international customer-facing IT and system transformation projects across mobility, retail, life sciences, and product development domains.

• Managed project opportunities and delivery initiatives from concept development, planning, execution, migration, validation, deployment, and final customer acceptance.

• Acted as the primary point of contact for project stakeholders, ensuring alignment between customers, business units, suppliers, and delivery teams.

• Ensured timely project execution through proactive monitoring of milestones, deliverables, dependencies, risks, and resource requirements.

• Led and coordinated global cross-functional teams of 20+ members across multiple time zones. Served as the central coordination point for project activities, ensuring accountability, milestones, progress tracking, and timely resolution of issues across multiple stakeholders. Managed status reporting with all the stakeholders, providing clear and timely updates to ensure alignment on project goals.

• Provided functional leadership, mentoring, coaching, and issue resolution support to project team members. Worked closely with senior leadership and executive stakeholders to define project charter and project scope, priorities, milestones, manage dependencies, success criteria and achieve project goals.

• Collaborated closely with senior leadership, business stakeholders, suppliers, operations teams, and external partners to define project objectives and ensure successful execution.

• Managed stakeholder communication through executive dashboards, KPIs, governance reviews, steering committee meetings, and project status reporting.

• Anticipated business needs and provided effective technical solutions in a timely manner to improve business satisfaction.

• Led and supported sub-project management structures, ensuring consistent execution, reporting, and escalation across complex initiatives.

• Managed supplier relationships and service providers to ensure compliance with contractual obligations, service levels, timelines, and quality expectations.

• Supported vendor selection processes, RFP evaluations, delivery governance, cost monitoring, and performance reviews.

• Controlled project budgets, forecasts, resource allocations, and financial tracking to ensure delivery within approved constraints.

• Scheduled workshops explaining the business requirement, prioritizing project goals and managing change.

• Oversaw cloud migrations (Azure/ AWS), database migrations/upgrades, system upgrades, infrastructure & application modernization, data management, legacy decommissioning initiatives, AI solutioning, transition plan of 2000+ applications etc.

• Coordinated execution, deployment, integration, commissioning, data migration and handover activities to ensure production readiness.

• Led a cross-functional team proactively managing dependencies and resolving project issues to closure, while maintaining delivery timelines.

• Proactively managed project risks, dependencies, issues, change requests, and escalations to ensure predictable project outcomes.

• Ensured compliance with GxP, ITIL, corporate governance standards, validation requirements, and audit controls.

• Led internal and external audit support activities, validation documentation, CAPA coordination, and regulatory compliance initiatives.

• Drove process standardization, automation, and governance improvements across project delivery functions.

• Led application rationalization initiatives resulting in the retirement of more than 50 legacy applications, improving operational efficiency and reducing maintenance costs.

• Currently driving Generative AI initiatives to automate project documentation, reporting, knowledge management, and delivery processes.

• Facilitated lessons learned workshops and implemented continuous improvement actions to enhance project execution effectiveness.

• Design and build of a fully isolated AWS offline environment with no outbound internet access for running a browser-based business application
• Design and implementation of a proxy and response service that terminates all external application calls inside the VPC and serves them from locally stored content; identification of the actual communication needs through measurement-based DNS query logging
• Creation of architecture designs and decision papers including a comparison of options (Application Load Balancer with Lambda and S3, reverse proxy on EC2, private API Gateway) assessed by operational effort, cost, and availability
• Transfer of the solution and operations documentation previously available only for Azure to an AWS target architecture, including reassignment of all services and operational processes
• Automated rollout as Infrastructure as Code (Terraform, CloudFormation) with CI deployment via GitHub Actions, plus setup of private DNS zones and an internal certificate chain for operation without internet access
• Creation of architecture, deployment, and operations documentation and handover to the customer
• Build-up of a private cloud platform on OpenStack at provider TelemaxX with Terraform, including FortiGate HA clusters, FortiManager, and Kubernetes
• Introduction of Policy as Code (Open Policy Agent, Conftest) as well as development of MCP servers (Model Context Protocol) to connect AI assistants to operations and project tools

Successes:

• Made the business application fully operable without internet access for the first time; the cause of the loading error was narrowed down systematically to missing CORS headers after the likely certificate issue was ruled out
• Fully transferred an existing Azure concept to AWS and replaced the manually created environment with a reproducible, CI-based rollout

Technology stack: AWS (VPC, Application Load Balancer, Lambda, S3, Route 53 private hosted zones and Resolver query logging, IAM, CloudWatch, EC2, CloudFormation), Infrastructure as Code (Terraform, CloudFormation, Remote State), CI/CD (GitHub Actions with OIDC, Azure DevOps Pipelines), OpenStack, FortiGate, FortiManager, Kubernetes, Policy as Code (Open Policy Agent, Conftest), offline and air-gap architectures, PKI & certificates (internal CA, TLS, CRL/OCSP), DNS, network segmentation, Linux, Windows Server, Python, Bash, PowerShell, YAML, JSON, architecture design & decision papers, documentation (Confluence, Markdown), Generative & Agentic AI (Model Context Protocol, Agentic AI Coding Tools)

• Understand the customer’s business & technical requirements and translate them into system / technical requirements
• Design and implement Schrodinger’s applications on cloud systems, and experience convincing senior management and senior technical staff of the benefits of their journey with Schrodinger on the cloud
• Provide exceptional technical design and thought leadership, especially around AWS, GCP, and K8s architecture reviews, performance, high availability, cost, and security
• Deep understanding of the Well-Architected pillars and all best practices for building a secure, performant Schrodinger’s applications on the cloud platforms
• Lead technical workshops and advise customers on architectural and strategic IT decisions
• Ensure success in designing, building and migrating applications, software, and services on the cloud platforms
• Educate customers on best practices to ensure their solutions are designed for successful deployment in the cloud
• Work with other team members to ensure quality and customer success
• Define the tickets, tasks, and timelines of projects
• Collaborate with account managers to ensure that the projects are executed according to the defined plan and timeline
• Monitor the progress of the projects, identify risks and issues, and take proactive measures to mitigate them
• Lead and inspire cloud architect teams, provide guidance, and make critical decisions
• Facilitate effective communication and collaboration among team members
• Collaborate with Schrodinger’s managers to improve deployment, support, and configuration of Schrodinger’s applications
• Lead weekly standups and define priorities

• Overall responsibility for the project lifecycle from scope definition to completion
• Close collaboration with platform teams, IT leaders, and external service providers
• Application of SAFe principles and structured sprint work
• Creation of a migration roadmap with clear milestones
• Monitoring of the lifecycle: onboarding, repository migration, replication of permissions, and system tests
• Visualization of the architecture with PlantUML and Gliffy as well as documentation in Confluence
• Regular status reports and running knowledge transfer sessions

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).