AngularJS Experts in Munich

Conducted an independent technical assessment of an AI-enabled software platform, combining AI risk auditing, cybersecurity analysis and software architecture review. Reviewed application components, APIs, data flows, LLM integrations, retrieval-augmented generation, multi-agent orchestration, authentication, tenant isolation, dependency vulnerabilities, AI governance and model lifecycle controls. Authored a technical audit report with a structured risk register, source-code evidence, regulatory references, corrective actions, AI system inventory, risk classification, remediation roadmap and Key Risk Indicators.

Technologies: TypeScript, JavaScript, React, Next.js, Node.js, REST APIs, MongoDB, Redis, AWS serverless services, OAuth 2.0 / OpenID Connect, LLM APIs, RAG, vector search, multi-agent orchestration, Model Context Protocol, C4 architecture modelling, threat modelling, NIST AI RMF, ISO/IEC 42001, ISO/IEC 23894, OWASP guidance, MITRE ATLAS

Functional design and specification of key SAP GTS processes, including

• Compliance,
• EU Preference Management and
• Reporting. Translate business requirements into specifications ready for implementation by third-party development teams.

• Create a product and service portfolio delivering a 360 degree approach to digital corporate sustainability covering KPI management, CSRD reporting and sustainable strategy
• Create vision and business case
• Onboard and align business partners, negotiate contracts
• Set up partner network as self-organized agile team
• Co-develop product and service portfolio
• Coordinate sales and marketing, go-to-market strategy and roadmap

• As part of developing this project, various apps were developed for an IBM portal environment based on portlets and Spring Boot services, as well as the setup of a BI platform based on QlikView

• Full-stack development of applications, technical analysis, refinement of requirements with the business department, coordination with the customer, DevOps

• Team size: 8 people

• Technology: Java 11.0, Maven, Git, Spring, Hibernate, JUNIT, Mockito, React, Jira, SQL, Spring Boot, CD/CI Jenkins, Kanban, Rest Api, Oracle

• Environment: Eclipse, IBM Rational Application Developer

• Further development of the internal "ECockpit" platform with an Angular 17 frontend and .NET (C#) backend
• Maintenance and further development of Azure DevOps pipelines
• Introduction of technical improvements in build & release processes
• Collaboration on a modular architecture approach (Clean Architecture & DDD)
• Focus on scalability and secure data processing
• Tech stack: Angular 17, .NET / C#, Azure, Azure DevOps, Git, CI/CD, Clean Architecture, Domain Driven Design

• Backend development of the cloud-based microservice
• Frontend development of the microfrontend
• Migration of the cloud backend environment
• Rollout of the distributed high-availability Privacy Data Service
• Technologies: Java, Kotlin, Spring Boot, REST Services, Kubernetes, Microsoft Azure, Cloud Security, OpenAPI, Postman, JavaScript, Vue.js, AngularJS, TypeScript, Micro Frontend, Redis, Kibana, Grafana, CI/CD, GitHub Actions, Jenkins, Helm Charts, Sec-Hub, Black Duck, Docker, Maven, Git, Scrum

• Participating in the development of Back (Quarkus) and Front (Angular) modules for Lufthansa travel Website and the “Bahn Project”.
• Providing technical architectures and solutions for the pre-build modules.
• Documenting the whole process of Reservation and Payment for the “Bahn Project”.
• Creating several unit tests and providing assistance for the CI/CD process.

• Requirements analysis and planning of the software architecture
• Analysis and design of REST APIs
• Backend development with Java 17, Spring Boot, Spring MVC, and Spring Data
• Frontend development with Angular and TypeScript
• Setting up CI/CD pipelines
• Code review, QA, and testing
• Using MySQL, Docker, the ELK stack, and RabbitMQ

• Successfully migrated over 150 BI dashboards (Jira, Microstrategy, AWS QuickSight) as project lead of 4 developers, ensuring a smooth transition and maintaining data integrity.

• Senior Frontend Developer, January 2022 – July 2024: Industry: banking and Deutsche Bahn, large databases; Technology: Angular 18, AngularJS, TypeScript, JavaScript, RxJS, CSS, HTML, Azure, Docker, Git; Company size: 50–55 people
• Senior Frontend Developer, October 2021 – December 2021: Industry: contract management; Technology: Angular 12, Syncfusion, TypeScript, RxJS, CSS, HTML, Docker, Git; Company size: 15–20 people
• Senior Frontend Developer, May 2021 – September 2021: Industry: web, social network; Technology: JavaScript, jQuery, Django, CSS, HTML, Git, Compass; Company size: 15–20 people
• Senior Frontend Developer, January 2021 – April 2021: Industry: web, web applications; Technology: Angular 8, TypeScript, RxJS, Docker, Git, CSS, HTML; Company size: 1–10 people
• Senior Frontend Developer, June 2020 – December 2020: Industry: web, web applications; Technology: JavaScript, Dart, Flutter, Git, CSS, HTML; Company size: 1–10 people

• Platform: Python ecosystem, RHEL 8, K10, AWS IoT Core, AWS Lambda, MLOps
• Software: Java JEE/cloud, IntelliJ IDEA, AWS IoT Core, AWS Edge and Lambda, AWS SageMaker SDK, Docker Compose, Kubernetes, OpenShift 4, Tekton, Flux, Helm charts, JSON/XML technology, Nginx, Apache Spark, OpenAI (GPT Plus, DALL-E 3, Whisper), GAN, GitHub Copilot, AI/machine and deep learning, Jupyter notebooks, TensorFlow 2, Colab, Keras API, Prometheus, Grafana, Conda, Python 3.9, PySci stack (NumPy, pandas, Scikit-learn, matplotlib)
• Responsible for webinar:
• IoT edge computing: architecture, components, resources, management
• IoT edge computing with MicroK8s, designing and creating flows/diagrams for AWS, three-step model for IoT ecosystem
• IoT processes, connectivity, data transfer and deployment, security
• Optimization of edge computing for IoT networks and services (AWS SQS queue, SNS notifications, events, analytics, buttons, device management/defender, Things Graph)
• Machine/deep learning frameworks (models, training, pipeline optimization, deployment in the cloud/at the edge (OpenShift), monitoring workloads with Prometheus and Grafana)
• Performance optimization for low latency/resilience using adaptive ML/DL/RL models for customer IoT data
• Analysis of large sensor data sets with Apache Spark, Kafka clusters
• Kasten K10 data management platform on Kubernetes multi-cluster with Helm chart, deployment, backup/disaster recovery (RTO/RPO), data lifecycle and security management
• Implementation of multilayer artificial neural network (ANN) with TensorFlow 2 and Colab for regression and classification; data analysis and provisioning for applications; development of models for testing and training, deployment of models
• Automation of business streamline processes with AI (Azure OpenAI, Discord bots/Zapier apps AI assistants (IntelliJ, GitHub Copilot))

As part of an in-house development project, a pipeline for automated lead generation was created as the second stage of a preceding system for project monitoring. Based on a list of freelance project URLs (freelancermap), qualified lead records are generated, including company, contact person, official and personal email address, and the appropriate form of address (informal/formal). The technologies used were Python (openpyxl, requests) as well as an LLM agent workflow in VS Code (GitHub Copilot Chat) with a custom slash command and extensive rule set; search APIs (Serper.dev) are connected for research. My tasks included the full concept and development. The core is a rule set of around 480 lines that guides the LLM agent deterministically through extraction, website and email lookup, duplicate matching (against existing provider lists), and the creation of a structured JSON output. Deterministic steps (Excel matching, web/email search, pattern derivation) were moved into Python helper scripts. Other requirements included validating the results through versioned blind runs against a reference, iterative refinement of the rule set, and a swappable search provider layer for cost and stability reasons.