WebSockets Experts in Germany

Temporary employment

• System black-box integration tests (BBIT, IVVQ): Execution of regression, release, acceptance, and compliance tests for safety-critical brake control units in the rail industry
• Software test application & integration: Runtime configuration of software components and libraries, validation of interfaces, configuration dependencies, and component interactions
• Test automation (FEAT framework): Co-development and further development of an automated test framework for test execution, reporting, and result analysis
• Functional safety (SiL4, FuSi): Ensuring compliance with safety requirements, traceability and coverage, as well as standards compliance according to EN50126/28/29
• Test automation for communication components: Configuration and validation of fieldbus (CAN) and Ethernet-based TCMS data communication interfaces (TRDP and CIP)
• Requirements analysis & shift-left (PTC Windchill ALM): Analysis of software and system artifacts to identify gaps, ambiguities, and redundancies early in the SDLC
• Test design & test case development: Derivation of test conditions, coverage strategies, and implementation of data-driven test cases (DDT), including reusable test data fixtures
• CI/CD & automation (Python, PowerShell, Jenkins, SVN): Automation of build, test, and HIL deployment processes as well as integration into CI/CD pipelines
• Test data & configuration management (XML): Maintenance and adaptation of XML test vectors and system configurations with automated integration into test environments
• Non-functional testing: Execution of performance and load tests to assess stability and system behavior
• Agile development & defect management (JIRA, Confluence): Participation in Scrum teams, test coordination, review of test artifacts, as well as defect tracking and root-cause analysis
• Error analysis & debugging (CANoe, CANalyzer): Analysis of errors and message flows across multiple system layers (application to bus)
• Model-based analysis (UML, Enterprise Architect): Specification of SUT/SOW and support for systematic test control
• Process & test documentation: Creation of integration and test documentation according to internal quality and certification requirements

• Designing the audit logging framework
• Implementing APIs for developers to integrate in their codebase
• Implementing ingestion pipeline, database query layer and UI for browsing the audit events
• Improving stability and reliability of the backend system

Development and further development of an internal platform for managing and providing technical resources, virtual machines, and infrastructure services. The platform supports self-service processes and covers functions that are conceptually comparable to cloud management solutions like Azure or AWS.

• Responsible involvement in the design, development, and implementation of new backend and frontend features
• Hands-on development with Java, Spring Boot, Python, and Angular
• Implementation of REST interfaces, business logic, validations, and integrations into existing system landscapes
• Further development of modern web interfaces with Angular, including connection to backend services
• Participation in architecture and design decisions within the team, especially with regard to scalability, maintainability, and clean interfaces
• Containerization and deployment of applications with Docker, Kubernetes, and Helm
• Support with CI/CD processes and deployment to Kubernetes-based environments
• Work in the environment of vSphere, Broadcom, GitLab CI/CD, ArgoCD, Maven, npm, and NuGet
• Close collaboration with developers, business teams, DevOps, and other technical stakeholders
• Analysis of technical requirements, deriving suitable solutions, and independent implementation in an agile team
• Use of GitHub Copilot to support code generation, refactoring, test case creation, and technical documentation

Methods/ tools/ technologies: Languages & frameworks: Java (21), Spring Boot (4.x), Python, Angular, Robot Framework, Kubernetes, Helm Persistence: PostgreSQL, MongoDB, Hibernate, Liquibase Architecture & communication: REST, gRPC, GraphQL, Apache Kafka, OpenAPI, Microservices, Event Driven, Domain Driven Design Cloud & infrastructure: Terraform, Docker, Rancher, Helm, Ansible Security: OAuth2, MS (Entra ID), web security, Keycloak (extensions for detailed group rights) DevOps: GitLab CI/CD, Ansible, Maven, Gradle, Grafana, Prometheus, Git, GitHub Copilot Testing & QM: JUnit, Robot Framework, automated component and integration tests, E2E tests with Playwright, Testcontainers, EasyMock Methodology & approach: Kanban, JIRA, Confluence, Clean Code

• Own full-stack development of an AI-native enterprise platform built on TypeScript, React, Vite, tRPC, Hono, and PostgreSQL, delivering AI-powered consulting workflows to B2B clients.
• Designed and shipped a multi-agent AI system using ReAct framework and Claude skills-style workflow patterns, including an intelligent PM assistant with rich system prompts, slash commands, tool integrations, and streaming chat UI.
• Architected an LLM evaluation framework: rubric-based LLM-as-judge, golden datasets, regression testing, and automated quality gating — ensuring consistent AI output quality at scale.
• Integrated LangFuse for end-to-end LLM tracing, conversation replays, and evaluation pipelines, enabling data-driven prompt optimisation that reduced token costs and response variance.
• Built with Drizzle ORM, pgvector, and knowledge graphs for structured data access, semantic search, and relationship-aware AI reasoning across the platform.
• Led TanStack React Query migration across the application — replacing manual state management with centralised caching and automatic refetching, reducing data-fetching boilerplate significantly.
• Practiced AI-native development throughout: Claude Code, Codex, Perplexity SDK, and LLM-assisted testing across the full development lifecycle. Deployed on Vercel + Azure ACA with Biome for linting/formatting.

• Further development and operation of a central backend service for providing vehicle inventory for international Mercedes-Benz online shops
• Further development of a reservation service for vehicles as part of the checkout process
• Design and implementation of a highly scalable end-to-end test architecture with a focus on maintainability, reusability, and a high number of automated test cases
• Development of a multi-layer test infrastructure with a strict separation of test logic and access layers
• Development of an initialization and caching architecture to significantly speed up local and CI/CD-based test runs
• Development of an AI-supported review architecture for automated evaluation and quality assurance of end-to-end tests
• Development of a dashboard for consolidated display of a vehicle context across multiple backend systems, including AI-generated summaries and compact case analyses
• Integration and further development of connections to various reservation systems via Apache Kafka and REST
• Design of new microservices and support with architecture decisions
• Risk analysis and design of microservice migrations
• Technologies: Java, Spring, Spring Boot, Gradle, Maven, Apache Kafka, REST, OpenAPI, Swagger, Github, Confluence, CI/CD, Microservices, AI, LLMs

Manufacturing intelligence platform with enterprise workflows, RAG, real-time AI assistant features, and multi-repository backend architecture.

• Built and extended production AI/backend services with Django, DRF, FastAPI, GraphQL, Celery, PostgreSQL, MySQL, Redis, and WebSockets across a modular multi-repository platform.
• Contributed to ARIS V2, a real-time manufacturing AI assistant using LangChain, LangGraph, MCP tool orchestration, planning/execution flows, OpenAI, AWS Bedrock, Qdrant, and Elasticsearch/OpenSearch-backed retrieval.
• Supported rollout expansion from ARIS V1 in 4 of 17 client production plants to ARIS V2 currently active in 13 of 17 plants, increasing real-world deployment coverage to more than 50% of the client footprint.
• Worked on document-grounded RAG functionality including ingestion, OCR, chunking, embeddings, indexing, retrieval, reranking, and grounded answer generation for industrial workflows.

Stack: Python, Django, DRF, FastAPI, LangChain, LangGraph, GraphQL, Celery, WebSockets, OpenAI, AWS Bedrock, Qdrant, Elasticsearch/OpenSearch, PostgreSQL, MySQL, Redis, Docker.

• Complete greenfield rebuild across the whole stack — built AI-native: backend in Go and NestJS, PostgreSQL (CNPG) on K3s with GitOps/Terraform; frontend on Angular 22, zoneless.
• Orchestrated coding agents (e.g. Claude Code, Cursor) across the entire lifecycle — architecture, implementation, testing, reviews, documentation — driven by Specification-Driven Development (SDD).
• “Bruno” — LLM commentator persona backed by RAG and MCP for a personality that stays consistent across all generations (match previews, post-match reports, his own virtual bets).

Angular 22 (zoneless, without Zone.js), Claude Code, Claude Code Skills, CNPG, Cursor, Design Tokens (Spec for Code), Docker, Gherkin, Git, GitLab, GitOps, Go, Google Gemini, Grafana, Hetzner Cloud, K3s, Keycloak, Kubernetes, Lighthouse, LLM Integration, Model Context Protocol (MCP), NestJS, Node.js, NPM, Playwright, PostgreSQL, Prometheus, RAG, REST, Specification-Driven Development (SDD), Structured Outputs, Terraform, TypeScript, Vitest

PLANT-MY-TREE®-per-order

The application enables Shopify merchants to automatically place tree-planting orders for every incoming order. By integrating ecological contributions directly into the purchase process, the manual effort for tracking and billing reforestation initiatives is eliminated. The system increases transparency for end customers through real-time visualizations of the ecological impact directly in the storefront. The architecture is based on a modular monolith with Spring Boot in the backend and an integrated React app inside the Shopify admin area. The solution uses webhooks to capture order data in an event-driven way and integrates the weclapp ERP system for automated monthly invoicing. An app proxy mechanism provides dynamic statistics such as CO2 compensation and planted trees without any performance loss for the merchant shop.

Tasks:

• Design of the modular software architecture based on Spring Modulith to ensure high maintainability
• Development of the event-driven business logic for evaluating Shopify orders via webhooks
• Implementation of automated invoicing by connecting the weclapp REST API
• Building the frontend using React Router and Shopify App Bridge for native integration
• Design of the database model and implementation of the persistence layer with JPA/Hibernate and Prisma
• Integration of internationalization processes for global use in the frontend and email communication
• Automation of deployment processes using Docker and GitLab CI/CD

Project skills: Java 25, Spring Boot, Spring Security, Spring Modulith, Hibernate, JPA, REST API, PostgreSQL, Maven, Liquibase, React, TypeScript, React Router, Vite, Node.js, Prisma, Zod, Docker, Docker Compose, GitLab CI/CD, Shopify CLI, Shopify App Bridge, Polaris, weclapp, i18next, Lombok, Vitest

• Expert-supervised AI systems for scientific reasoning, model evaluation, and research workflows.
• Built the IAS Problem Solver, an orchestrated system for difficult mathematical reasoning; it achieved 84% in one submitted answer set on the Leipzig mathematics benchmark.
• Built a resumable state-machine pipeline for research-grade mathematics benchmark generation: source selection, LLM-agent-based phenomenon discovery, task synthesis, gold-answer and certificate generation and validation, probing, repair, human feedback, and quality gates, targeting tasks that are difficult, natural, verifiable, and cost-effective.
• Current work extends this into budget-aware AI research workflows for real scientific problems with expert review.

Tech stack: Python, OpenAI/OpenRouter-compatible APIs, embeddings, RAG, SQLite.

• Founded an AI-powered scheduling and business-management SaaS for SMBs, owning technology strategy, architecture, product development, UX, billing and go-to-market execution.
• Architected and shipped a multi-tenant platform with REST APIs, RBAC, CRM, billing and notifications, powering the manager dashboard, admin console, booking experience and iOS/Android applications.
• Led and mentored 7 software engineers, 1 DevOps engineer, 1 QA engineer and 1 UX/UI designer, while remaining hands-on across backend, frontend and product delivery.
• Built AI voice and chat agents using Python/FastAPI, OpenAI and Anthropic APIs, RAG, pgvector and tool calling; integrated Twilio, Google Calendar/Meet, Stripe and Firebase.
• Owned production infrastructure and automated delivery across separate environments using Docker, Nginx, GitHub Actions and Grafana; represented the company at accelerators and international startup events.

Selected stack: Python, FastAPI, Node.js, Vue 3, React/Next.js, React Native, PostgreSQL, Redis, Docker

• Provided technical leadership for a 4-engineer team delivering 3 major client platforms in 12 months with microservices architecture and scalability solutions — 100% of scoped majors shipped ahead of schedule vs. planned milestones (baseline: prior releases often slipped 1–2 sprints).
• Ran AI model evaluation and model outputs evaluation on LLM/AI vendor APIs: safety, completeness, instruction adherence, and groundedness review before go-live; cut escaped bad outputs in AI-integrated release checklists from recurring UAT findings to near-zero on final promote.
• Drove API development and performance optimization for payment, exchange, and AI services; fail-closed error handling and payload validation reduced integration rework cycles by ~35% vs. the first AI integration pass.
• Applied software testing, testing frameworks, code quality assurance, and code refactoring with continuous integration gates; first-pass PR acceptance improved across the team and production hotfixes on AI adapters dropped noticeably after review standards landed.
• Owned DevOps practices: Docker, GitHub Actions, Jenkins-compatible pipelines, and version control workflows — cut deployment time ~50% vs. pre-automation baseline and stabilized releases across 3 client environments.
• Implemented verifier/oracle-style pass-fail checks in container sandboxes (Harbor/Terminal-Bench aligned); wrote technical documentation so failures cleared in one review cycle.
• Led cross-functional collaboration with product and client stakeholders; translated AI evaluation scores and risk findings into plain-language briefs for non-technical partners, unblocking go/no-go decisions without extra engineering meetings.
• Used agile methodologies for sprint planning and backlog ownership; mentored engineers so mid-level contributors owned AI adapter modules independently by mid-engagement.

• Built a complete real-time multiplayer platform with game engine, lobby system, game tables, spectator mode, and persistent game history.
• Implemented product-relevant multiplayer workflows with WebSocket communication, live synchronization, voice chat, mobile app features, and push notifications.
• Developed a scalable full-stack architecture with authentication, PostgreSQL persistence, premium/billing integration, and extensive test coverage.

Development of a route planner for incident communication.

• Development of the REST API
• Set up a patch system for maintaining the routing graph
• Expansion of the testing infrastructure
• Performance and memory optimization (JMeter, JFR)

Technologies: Java 21, Spring Boot, JGraphT, Flyway, MapStruct, Caffeine, ShedLock, JMeter, Kubernetes, JFR

Built charts for historical data, alarms, and downtime, plus enhanced interactive tables to efficiently track and compare oil-well performance. Eliminated 80% of high/medium security risks (Checkmarx) for the app. Optimized charting libraries, cutting usage from to 300–400MB for large data sets.