AWS Elastic Beanstalk Experts in Germany

• Role: Simulation Engineer
• Environment: Mechanical Engineering / Visual Inspection
• Goals & Implementation: Simulation of photometric-stereo setups to determine the best positions for cameras and light sources for each specific part.
• Business Value: Enabled a low-cost and scalable solution for determining part-specific hardware setups.
• Tech Stack: Python, Blender

• Development and optimization of the RTL+ frontend application for SmartTV and set-top box platforms with React and Next.js.
• Key role in the technical coordination of developers within the team and in coordinating implementation.
• Central interface to adjacent teams to simplify development processes and improve cross-team alignment.
• Improved frontend performance, stability, and rendering behavior on low-powered devices in a restricted runtime environment.
• Implemented a frontend testing strategy with Jest, React Testing Library, and Playwright.
• Implemented accessibility improvements according to WCAG 2.2 and WAI-ARIA.
• Integrated Didomi Consent Management as a contribution to increasing ad monetization on streaming platforms.
• Used AI-supported engineering workflows with Cursor for structured implementation, refactoring, and faster problem solving.

Technologies used: React, Next.js, TypeScript, JavaScript, GraphQL, Apollo Gateway, Zustand, Tailwind CSS, Styled Components, React Testing Library, Playwright, Jest, HTML5, CSS3, AWS Lambda, EC2, CloudFront, S3, GitLab CI/CD, NX, Cursor

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

• 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

• 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

• Environment: AWS, Ruby on Rails, GraphQL, React Native
• Penetration test for Backend, Android-App and iOS-App

• Extension and maintenance of the Doktree tax advisory document management tool
• Alfresco and Angular development
• Linux system maintenance

• Migrated the Billomat API integration to InvoiceNinja API, boosting system reliability by 33% for 1300 users.
• Established an ESLint configuration with Husky and lint-staged, resulting in a 23% decrease in code review issues and refined code consistency across 5 repositories.
• Improved team processes by refining the Kanban workflow.
• Optimized frontend performance, built a new CI/CD pipeline, and implemented AWS resource monitoring to strengthen deployment reliability and system observability.

• Development of dynamic web components for displaying KPIs, intelligent map applications, and operational process analysis tools
• Angular 18+
• Leaflet, MapLibre
• NestJS, JavaScript, HTML, CSS
• PostgreSQL, GraphQL, RabbitMQ
• Gitea, Jenkins, Docker

• Worked with a UK based organisation offering a range of free sexual health services such as STI testing, treatment and contraception across the UK
• Created a Liferay DXP based customer facing portal (version 7.4) over Liferay Experience Cloud
• Designed and developed Liferay modules
• Managed Liferay Experience Cloud and release management
• Coordinated with different teams and helped other team members on Liferay challenges
• Engaged in discussions with business and stakeholders on expectations
• Implemented architectural best practices

• Provision Kubernetes infrastructure on Azure, GCP, or bare-metal at the hosting provider
• Design and implementation of REST and GraphQL APIs
• Cloud governance (cost, compliance, security)
• Infrastructure-as-Code and CI/CD as code (Terraform, Ansible)
• Container technologies (Docker, Helm)
• Maintain and improve CI/CD pipelines (GitLab CI, GitHub Actions), Argo CD
• Deployment, scaling, and monitoring of microservices
• Distributed event streaming (Kafka)
• Message brokers RabbitMQ, MQTT
• Software development from scratch or further development of existing projects using PHP, Python, Go, C#, and Bash

Consulting, designing, and implementing the software testing lifecycle for multiple Android apps and backends Introducing test automation with Appium, SpringBootTest, RestAssured Setting up and managing emulators Developing an E2E library, an integration library, a Testcontainers library, and other libraries Developing, implementing, and advising on CI strategies Building the test environment with Testcontainers and virtualization Developing and implementing test cases for integration tests and end-to-end tests Establishing a maintainable test architecture with low entry barriers to simplify the creation of automated tests Some tools and technologies used: JUnit, RestAssured, Testcontainers, Kubedock, Appium, Android emulator, Jira Xray, Mockito, WireMock, Spring Framework, Kotlin

• Solution for micro-service and frontend solution for the electric grid
• Leading of development teams
• Technologies: React-Native, TypeScript, Kotlin, AWS, Infrastructure As Code, Serverless Architecture, React, NoSQL, GraphQL, Angular, Playwright, Python, ML(Ops), Kubernetes, Docker