Real-Time Analytics Expert in Germany

• 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

• AI Integration (LLM & RAG): Design and build of an internal intelligent RAG system (Retrieval-Augmented Generation) based on LLMs, n8n, and vector data for the automated analysis of technical documents and error logs.
• Design & Implementation: Design of a robust RS-232/UART communication interface for an SBC-based embedded device to control medical shaker systems.
• Architecture & Protocol Design: Implementation of a highly maintainable software structure (OOP, SOLID) and definition of hardware-close, resilient communication protocols including multithreading and advanced error handling.
• Quality Assurance & DevOps: Test automation using xUnit, integration tests directly on the hardware target, and maintenance of technical documentation according to strict medical technology standards via Azure DevOps.

Label: C#, .NET, LLMs, RAG, n8n, RS-232, UART, Multithreading, async/await, xUnit, gRPC/protobuf, Blazor, MudBlazor, EF Core, Visual Studio 2026, Azure DevOps

• Delivered AI and data science solutions, including LLM-based chatbots and data pipelines, improving operational efficiency.
• Collaborated on product features, achieving measurable impact and maintaining strong client relationships.

• Contributed to the development of an advanced RAG AI Chat application that integrates multiple LLM models, enabling users to seamlessly switch between models based on specific tasks. This improved the user experience by providing tailored, efficient solutions for various use cases, such as event scheduling, booking systems, and complex task management.
• Participated in designing and implementing a robust backend architecture using Spring AI, enabling advanced AI-driven capabilities like intelligent task automation, language processing, and contextual recommendations. Leveraged Spring AI Tools and Advisors to enhance the performance and decision-making of the AI models.
• Collaborated on the integration of vector databases to support embeddings, enhancing the app's ability to understand user queries and perform actions based on complex, real-time data inputs.
• Contributed to the development of a seamless, user-friendly front-end interface using React with TypeScript support, ensuring a modern, responsive, and scalable user experience across platforms.
• Assisted in implementing state management with Redux RTK for efficient data flow and real-time updates, optimizing the overall user experience in dynamic scenarios such as scheduling and task management.
• Partnered with stakeholders to define feature requirements, helping ensure that the app could scale to meet evolving business needs and integrate with other systems like calendar and email services. Worked alongside cross-functional teams, including data scientists and UI/UX designers, to fine-tune AI models and ensure alignment with project goals.
• Contributed to ensuring end-to-end system performance, security, and compliance by helping integrate authentication mechanisms, role-based access control, and secure communication protocols in both backend and frontend layers.

Technology Stack and Key Contributions:

• Java / Spring Boot / Spring AI: Developed backend services leveraging Spring AI for intelligent responses, task automation, and complex workflows.
• React / TypeScript: Built intuitive user interfaces with React and TypeScript, ensuring a smooth and scalable frontend.
• Redux RTK: Managed application state with Redux RTK for optimized state management, enabling dynamic, real-time data updates.
• Vector Databases: Integrated vector databases (pgVector) for embedding support, improving AI model performance in handling complex queries.
• PostgreSQL / Redis: Managed persistent and temporary data with relational and in-memory data stores, ensuring data integrity and speed.
• Kafka: Utilized Apache Kafka for event-driven communication and seamless integration between microservices.
• Spring Security: Ensured the security of backend services with robust authentication and authorization mechanisms.
• CI/CD & DevOps: Integrated continuous integration and deployment pipelines to ensure rapid and secure deployment of features and updates.

• Regulated environment within a German banking group (approx. 8,500 employees, hybrid cloud strategy).

• Responsible for operating, provisioning, and continuously securing business-critical platforms – including a GenAI chat application, a big data/AI platform, and data science workspaces based on Azure Virtual Desktops and VMs. Ownership of Azure DevOps projects for ShaiHulud and React2Shell, as well as BSI alerts – Security Operations improvements across the SDLC.

• Deployment responsibility for the GenAI chat application, big data/AI platform (BDAI), and data science workspaces (AVD/VM-based) in the respective landing zones.

• Deployment & release management: end-to-end responsibility for deploying portal and service applications across multiple Azure landing zones, including technical approvals, compliance with development team deployment guidelines, and ensuring ITIL-based change and release processes via ServiceNow.

• Azure landing zones & network architecture: design, provisioning, and operation of Azure landing zones for 3-tier web applications with enhanced network segmentation, VNet peering, hub-and-spoke architectures, private endpoints, and firewall integration across separate subscriptions and tenants.

• Azure DevOps governance & operations: ownership of the Azure DevOps organization, including projects, repositories, and CI/CD pipelines; implementation of governance requirements such as branch policies, approval gates, permission models, and audit-ready operating structures.

• Infrastructure as Code (Terraform): design, implementation, and operation of a modular Terraform architecture for standardized cloud infrastructure deployment, including state management, provider versioning, reusability, and policy-as-code approaches.

• CI/CD pipeline engineering: design, operation, and optimization of complex YAML-based CI/CD pipelines with multi-stage deployments, template standardization, self-hosted agents, integrated secret management, and automated quality and security checks.

• Git migration & platform consolidation: planning and execution of repository and pipeline migration from Azure DevOps to GitLab CI/CD, including automated scripts, full Git history transfer, pipeline porting, and platform consolidation.

• Container & platform operations (AKS): operation and security assessment of containerized workloads on Azure Kubernetes Service, centralization of on-premises container registries for ACR.

• OpenShift (OCP) security reviews: security assessment of code baselines, build pipelines, and deployment processes for on-premises OpenShift clusters with critical applications, and derivation of specific hardening recommendations.

• Shift-left security & DevSecOps transformation: introduction of a company-wide shift-left approach for early security integration in development and deployment processes, enabling developers to perform self-led security checks and sustainably reduce vulnerabilities before production (IDE integrations, pre-commit hooks, local scanners).

• Software supply chain security: analysis and mitigation of supply chain risks in NPM- and Yarn-based applications through dependency audits, CI/CD pipeline hardening, token rotation, and restriction of risky build and lifecycle mechanisms.

• Frontend & framework security (React / Next.js): security assessment and coordination of critical vulnerability remediation across platform applications and web frameworks, including coordination and complementary technical mitigations with all teams following BSI alerts.

• Software composition analysis (SCA): introduction and operation of automated vulnerability scans for container images, pipelines/artifacts, and third-party dependencies, including SBOM exports within CI/CD pipelines.

• SAST/DAST integration: design and piloting of static and dynamic application security tests in close collaboration with security architecture and development teams, for continuous improvement of code and runtime security, and establishing operational acceptance tests.

• Artifact & registry consolidation: analysis and consolidation of all package and container repositories for service applications and AKS workloads, aiming for a centralized, secured registry strategy with centralized vulnerability scanning and governance.

• Dependency-Track & SBOM strategy: advising the compliance board on introducing a central SBOM and vulnerability management platform to increase enterprise-wide dependency transparency and accelerate CVE response capability.

• CI/CD pipeline hardening: security analysis and cleanup of the existing pipeline landscape by removing unused pipelines, improving secrets hygiene, implementing least-privilege principles, and isolating build agent environments.

• Azure Web Application Firewall (WAF) optimization: analysis and tuning of existing Azure WAF rules (OWASP Top 10 Core Rule Set, DSR/SDC, custom rules) to defend against known vulnerabilities and exploit patterns, including reducing false positives and improving threat detection.

• Documentation & stakeholder communication: creating and maintaining technical documentation, runbooks, and architecture overviews in Jira and Confluence, as well as active knowledge transfer between operations, development, security, and compliance stakeholders.

PFS-Matching-App

• Integrated an intelligent LLM chatbot using LangChain4j, enabling conversational AI, context-aware question answering, document summarization, and autonomous tool execution.
• Implemented Retrieval-Augmented Generation (RAG), prompt engineering, and AI agent workflows to connect large language models with enterprise data and backend services.
• Developed RESTful APIs and secure backend services to support AI-driven interactions and business processes

• Elevated LLM output reliability by engineering domain-specific prompts and evaluation logic, improving reasoning consistency across production language model workflows.
• Designed advanced coding benchmarks and validated solutions to strengthen training and evaluation datasets, improving model performance on technical problem-solving tasks.
• Designed and implemented automated evaluation frameworks for technical reasoning tasks; optimized LLM output reliability by 15% through rigorous prompt engineering and rubric-based benchmarking.

• Built fault-tolerant cloud infrastructure for AI-powered machine monitoring
• Implemented ML models for object detection & analysis
• Automated deployments with GitHub Actions, Helm, and Kubernetes
• Tech stack: Python, TensorFlow, Kubernetes, AWS, Prometheus, GitHub Actions

• Embedded product strategy into a delivery-focused agency, reducing scope creep and increasing product velocity.
• Owned cross-functional delivery processes, from discovery to MVP rollout across e-commerce and event platforms.
• Formalised product rituals (epics, metrics, reviews) for multiple B2B clients.

• Integrated complex AI-based energy system models into the frontend framework, enabling the visualization of insights for 6+ key clients and maximizing energy utilization.
• Maximized energy efficiency and utilization by architecting the seamless data flow between AI models and the user interface for rapid, actionable reporting.

• Technical consulting for greenfield development of AVIATAR 2.0
• Designing data pipelines and data architecture within the DataVerse Data Lake
• Planning and implementing Data Lake zones, data governance, data retention, and recovery strategies
• Collaboration with the Data Architecture and Platform Team for infrastructure scaling and design
• Close cooperation with stakeholders across Lufthansa Technik, including requirements management
• Hands-on development of data pipelines using Spark Structured Streaming and Databricks
• Proofs of Concept (PoCs) for Graph-Links and real-time analytics
• Provisioning data for AI and ML projects (e.g., predictive analytics)
• Technical design and implementation of near-real-time pipelines based on business requirements

• Directed global IT teams in implementing cloud-based, AI-driven solutions, aligning with customer requirements through comprehensive data migration strategies
• Led cloud migration initiatives from legacy systems to scalable cloud platforms, streamlining data handling through API-driven processes to enable real-time analysis and reporting
• Enhanced reporting accuracy and customer satisfaction using machine learning and neural networks, achieving 97% classification accuracy
• Advanced projects using augmented reality (AR) to optimise user experience and meet client goals

• Developed ASR and TSR-based speech processing pipelines on AWS, enabling efficient feature extraction and scalable deployment for speech and text analytics.
• Built a Multimodal Speech Emotion Recognition system combining NLP and deep learning (audio + text), achieving 98% accuracy and supporting real-time, cloud-based inference.
• Designed and optimized end-to-end model training and evaluation workflows using AWS services (S3, EC2, Lambda) to ensure performance, reliability, and reproducibility.
• Created and deployed interactive, user-friendly dashboards for data visualization and insight generation, supporting research teams and management in data-driven decision-making.

• Spearheaded the development of BW Bridge and Datasphere models, integrating S/4HANA, BW/4HANA, and BDC to enable real-time analytics in SAP Analytics Cloud (SAC).
• Designed and implemented Calculation Views using SAP HANA Modeler, aligning with LEONI's requirement for expertise in BW/4HANA modeling and Eclipse.
• Authored comprehensive technical documentation and RICEFW objects, ensuring clarity and alignment with business requirements.
• Conducted training sessions for internal teams on Datasphere and BW Bridge, fostering knowledge transfer and self-sufficiency.