AWS IAM Experts in Germany

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• Designed FastAPI backend with multi-turn conversation handler, token budgeting, and context window management.
• Integrated Amazon Bedrock (Claude 3 Sonnet/Haiku); built RAG pipeline with MongoDB chat history and semantic search.
• Implemented Factory Pattern for modular LLM provider switching; reduced model onboarding effort by 60%.
• Reduced LLM inference cost by 35% through model tiering (Haiku vs Sonnet) and prompt/entity consolidation.

Tech: Python, FastAPI, Amazon Bedrock, MongoDB, Streamlit, Pydantic.

• Developed backend features for Modern RIS, a web-based Radiology Information System integrated with the existing Classic RIS via WebView.
• Worked on a modular Spring Boot backend covering clinical workflows such as appointments, examinations, patients, orders, reporting, billing, and inventory.
• Contributed to event-driven architecture using domain events to decouple workflows across backend modules.
• Implemented REST/OpenAPI endpoints, service-layer business logic, DTO mapping, validation, and integration points for the React frontend.
• Worked with PostgreSQL-backed domain models, Liquibase database changes, read/write model separation, and legacy RIS database structures.
• Integrated authentication and authorization flows using Keycloak and OAuth2.
• Added and maintained unit/integration tests using JUnit, Rest Assured, Testcontainers, and project-specific test utilities.
• Supported CI/CD and local development workflows using Maven, Docker Compose, Jenkins, and generated OpenAPI clients.

Tech stack: Java 21, Spring Boot 3.5, Maven, PostgreSQL, Liquibase, Keycloak, OAuth2, REST, OpenAPI/Springdoc, MapStruct, Lombok, Docker, Testcontainers, Jenkins.

• 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).

• Technical leadership and architecture for enterprise clients (including LR Health & Beauty, Deutsche Bahn, Trusted Shops)
• Cloud migration, microservices architectures, CI/CD optimization

• Analysis
• Consulting
• Software design
• Development
• Automation
• Testing
• Deployment
• Architecture, development and deployment of various proof-of-concept applications around the integration of current AI interfaces including conversational, realtime voice, images and videos
• Developed best practices for working with agentic systems and AI in practice
• Created code templates

• Setup and operation of a multi-cluster AWS EKS platform for banking workloads with a unified network and security architecture across 45 AWS accounts.
• Developed and standardized a unified AWS network and security architecture for 45 AWS accounts, enabling consistent governance, connectivity, and compliance for enterprise customer environments.
• Developed and operated a multi-cluster AWS EKS platform to support production workloads, significantly improving scalability, availability, and operational reliability.
• Implemented a GitOps deployment model using ArgoCD and Helm, enabling fully automated, auditable deployments and reducing manual release errors.
• Automated infrastructure provisioning using Terraform and Terragrunt at scale, reducing environment setup time by up to 70% and eliminating configuration drift.
• Established enterprise-grade backup and disaster recovery strategies using Velero and AWS Backup, ensuring reliable multi-cluster recovery and business continuity.
• Introduced Rancher as a self-service Kubernetes platform, accelerating developer onboarding while maintaining centralized security and governance.
• Designed and implemented detailed AWS IAM concepts (roles, policies, trust relationships) to enforce the principle of least privilege for access to accounts, workloads, and CI/CD pipelines.
• Developed AWS Lambda-based pre-provisioning workflows for databases, automating initialization, configuration, and access setup to support secure and consistent application integration.
• Delivered consistent, high-quality results as part of a 5-person AWS Solutions Architecture team, resulting in three consecutive contract renewals.

• Responsible for setting up and configure AWS Organizations and Control Tower on company's master organizational account
• Administer and maintain various AWS services, including EC2, S3, RDS, Lambda, VPC, IAM, etc.
• Monitor system performance, availability, and capacity planning to ensure scalability and reliability
• Implement and maintain infrastructure as code (IaC) using tools like CloudFormation or Terraform
• Work closely with development and operations teams to automate deployment processes using CI/CD pipelines (e.g., Jenkins, GitLab CI/CD)
• Develop and maintain scripts for automating routine tasks and infrastructure provisioning
• Implement automation for monitoring, logging, and alerting to ensure timely incident response
• Implement and enforce security company guidelines and best practices for AWS environments
• Configure and manage AWS security services such as AWS Identity and Access Management (IAM), AWS WAF, AWS Shield, etc.
• Collaborate with the Security Team to improve and update security policies and posture
• Collaborate with development teams to provide agile deployments and optimize application performance and reliability on AWS
• Provide technical support and guidance to internal teams on AWS-related issues and best practices
• Participate in cross-functional projects to enhance overall infrastructure and operational efficiency

• Developed a comprehensive business concept for a medical fall detection device based on a patented process registered in Germany
• Identified and collected use cases for medical device deployment in residential buildings and healthcare provider facilities
• Expanded the product scope to industry standards such as HL7/FHIR and designed a product based on modern communication protocols for IIoT
• Supported offshoring activities, defined SLA and scope-of-work documents for development teams after selecting various vendors
• Identified and selected hardware components (Terrabee, E-Con Systems) for LIDAR/TDOA functions
• Defined integrated AI features and LLM models for patient fall detection as well as AI-based audio triggers
• Oversaw the implementation of algorithms for object detection, fall detection, and false alarm identification

• Role: Software Engineer & Applied Mathematician (Mathematical optimization for scheduling; duration: 1 months; team setting: Team of 2, remote; technologies: JuMP, Julia, Pluto, Svelte, JavaScript, TypeScript, JetBrains Space, Terraform, Nomad)

• Role: Software & Cloud & Web Engineer (Building scalable data science compute cluster from scratch; duration: 11 months; team setting: Team of 1, on-site; technologies: Terraform, Kubernetes, k8s ingress, k8s services, k8s RBAC, k8s networking, k3s, etcd, S3, DNS, certificates, Julia, Pluto, JavaScript, Tailwind, Astro, npm, Parcel, Preact, MUI, JWT, AWS SQS, AWS RDS, Python, GitLab, GitHub)

• Role: AI & Web Engineer (Custom ChatGPT service; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Poetry, LangChain, Tailwind, ChatGPT API, Flask, FastAPI)

• Role: Architect & Data Engineer (Central datalake setup and ingestion; duration: 9 months; team setting: Team of 5, remote; technologies: Infrastructure-as-code, AWS CDK, Python, Boto3, PySpark, AWS Glue, IAM, S3, ECS, Fargate, Lambda, Apache Hudi, DeltaLake, Databricks, GitHub, Jira, Miro)

• Role: Software Engineer (PoC Julia migration of scikit-decide; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Julia, GitHub)

• Lead Architect to design and implement the data lake and ETL Pipeline using AWS Stack
• Performance Optimization of Data Ingestion of ETL Pipeline
• Development of Data Validation using Great Expectations
• Leading of the data migration for two sources exchanges
• Data Modeling in AWS Redshift

MLOps

• Model inference implementation by mlflow and AWS SageMaker
• Feature Engineering for the running ML Models ( Recommender Engineer, Clustering )
• Implementatino of Model Registry and artifactory using mlflow
• Historization an Profiling of the Input Data Using AWS Glue Crawler and AWS Data Catalog
• Feature importance using mlflow

Tech. Stack: Python 3, AWS Glue, AWS Step Fucntion, AWS Lambda, AWS EventBridge, AWS IAM Role, AWS SageMaker, AWS EC2, AWS Glue Crawler, AWS CloudWatch, MLFlow, ETL, Data lake, GitHub Action, Terraform, Jenkins, Ansible playbooks (Infrastructure as Code), CI/CD, GitLab, SQL, PySparkSCRUM, Agile, Jira, BigData, VSCode, DBeaver, MSSQL, MySQL, grafana, Docker, Linux, Bash, MapReduce, Data Modeling (ORM), Pandas, YAML, SQL-Alchemy

• Extended the document management system with a standard CMIS (Content Management Interoperability Services) interface
• Implemented CMIS core services like navigation, access rights, search, CRUD operations, and versioning in Java
• Implemented based on RESTful / OpenAPI services
• Delivered as a fat-jar and native container image
• Deployed on-premises and serverlessly as an Azure Container Application using Terraform
• Improved team autonomy through infrastructure engineering and short feedback loops
• Established observability with OpenTelemetry, Azure Monitor, and Azure Logic Apps
• Introduced Terraform and trunk-based development processes
• Ensured quality with BDD tests in C# using SpecFlow and Testcontainers
• Created Azure DevOps pipeline integration tests
• Introduced cloud deployment processes
• Trained staff in cloud and Terraform

• Building the company with a focus on connectivity for IIoT and Industry 4.0, iRPA/process automation, advanced robotics and smart systems, sensors and services
• Project management and software architecture for IoT gateway development (since 2020) with protocol translation, IT/OT convergence and GRC
• Developing RPA bots for automating and monitoring industrial processes with an agent-based AI approach (since 2020)
• Implementing unsupervised clustering and anomaly detection for time series data in big data streaming pipelines (since 2021)
• Introducing a Docker-based release train for OTA updates with DevSecOps and CI/CD (since 2018)

• Conceptualizing & developing a need-to-know, domain-based identity and access management system in a high-security environment
• Backend development (Python): API & microservice development

• Led a team of 6 software engineers to develop and maintain an AI-driven healthcare platform, enabling automated diagnostics and prescriptions based on real-time ECG data analysis
• Designed and developed a robust Revenue Cycle Management (RCM) system, integrating HL7 and FHIR APIs to enable seamless interoperability, real-time data exchange, and HIPAA-compliant data handling, improving billing efficiency, claim processing, and regulatory adherence in healthcare operations
• Migrated a legacy monolithic application to a scalable microservices architecture, enhancing system modularity, scalability and maintainability while implementing key design patterns such as Strangler, Database-per-Service, API Gateway, Saga and CQRS for efficient service communication and transaction management
• Architected and led a C# 9/.NET 6 microservices ecosystem handling hotel reservations, payments, and loyalty programs, enabling 99.99% uptime across 10+ services
• Defined OpenAPI/Swagger contracts and auto-generated client SDKs, reducing front-to-backend integration time by 50%
• Containerized each service with Docker and orchestrated deployments via Kubernetes, slashing release lead time from days to hours
• Designed PostgreSQL schemas optimized for high-volume transactional workloads and implemented Redis caching layers to accelerate read-heavy endpoints by 80%
• Built Kafka streaming pipelines for real-time availability updates and audit logs, processing 2 million+ events per hour with end-to-end delivery guarantees
• Implemented unit and integration tests for React applications using Jest and React Testing Library, ensuring 80%+ test coverage, improving component reliability, and preventing regressions
• Defined and deployed AWS cloud infrastructure using Terraform, while containerizing and orchestrating microservices with Docker and Kubernetes, improving automation and system scalability
• Built a scalable full-stack booking application using React 18 and Django REST Framework, integrating Celery and Redis for asynchronous task processing, while deploying on GCP with Cloud Run and Firestore, enabling real-time scheduling, payment processing, and automated notifications
• Mentored junior developers through code reviews, pair programming, and knowledge-sharing sessions, improving team efficiency by 30% while maintaining comprehensive API documentation using Swagger/OpenAPI