Amazon Web Services Experts in Munich

• Analysis of affected business processes taking numerous stakeholders into account
• Interface analysis, architecture and system design
• Communicating and coordinating various subprojects and interface partners
• Creating epics and user stories, maintaining the backlog, workshops and review presentations
• Support during implementation between business departments and development
• Test management including strategy and approach definition
• Test case definition, execution and approval
• Cross-team organization of integration and acceptance tests
• Support of test environments
• Technologies and tools: Java, Angular, Kubectl, REST, AWS SNS/SQS, Kafka, S4/HANA, Bruno

• Deployment and scaling of existing ML initiatives, including demand and cash flow forecasts.
• Building robust monitoring with mlflow for data stability, model performance, and drift detection, as well as implementing additional ML use cases.
• Further development of an Agentic AI chatbot for transparent and easy-to-understand model explanations.

Unified ITSM framework

• Definition of a company-wide ITSM target picture
• Introduction of a uniform service structure across all business units

SLA and OLA management

• Building a standardized SLA framework
• Definition of service classes (Business Critical, Standard, Low Priority)
• Introduction of OLAs between internal teams
• Building meaningful SLA reporting
• Definition of KPI and service dashboards for business units

Service portfolio management

• Definition of service descriptions
• If needed, preparing possible cost and service billing

Ticketing & processes

• Incident management
• Uniform ticket categories
• Standardized prioritization
• Escalation matrix
• Automations
• Self-service optimization

Request fulfillment

• Service catalog across all business units
• Approval workflows

Problem management

• Introduction of root cause analysis
• Known error database
• Problem review process

Complete asset management concept

• Hardware lifecycle management
• Software lifecycle management
• Leasing lifecycle
• Mobile device lifecycle
• Monitor lifecycle
• Phone lifecycle

Processes

• Procurement
• Goods receipt
• Inventory
• Assignment
• Return
• Disposal
• Leasing return Goal: single source of truth for all assets

CMDB design

• Definition of all configuration items:
• Workplace
• Notebooks
• Monitors
• Mobile phones
• Printers

Infrastructure

• Servers
• Firewalls
• Switches
• WLAN
• Storage
• Backup systems

Cloud

• Azure resources
• Microsoft 365
• SaaS services

Relationships

• User ↔ Asset
• Asset ↔ Service
• Service ↔ Infrastructure
• Location ↔ Asset
• Goal: make all service dependencies visible

Software asset & license management

• License management concept
• License balancing
• Compliance reporting
• Microsoft license management
• Adobe license management
• SaaS management
• Contract management
• Renewal management

Interfaces & automation Existing systems

• Workday
• Joiner
• Mover
• Leaver

TESMA

• Leasing data
• Contract data

Matrix42

• Asset synchronization
• User synchronization

Active Directory / Entra ID

• User management

Microsoft 365

• License assignment
• Group management

Dormakaba

• Access processes

• Lifecycle services

Monitoring platforms

• PRTG
• Palo Alto
• Cisco

Reporting & KPI framework

• Definition of a management dashboard
• KPIs
• Ticket volume
• SLA fulfillment
• MTTR
• First resolution rate
• Asset accuracy
• License compliance
• Change success rate
• Service availability
• Degree of automation

Network redesign support

• Governance
• Support of the network redesign from an ITSM point of view
• Definition of affected services
• Change management structure
• Communication concept

CMDB integration

• Recording of all network components
• Service mapping
• Dependency analysis

Validation of documentation and knowledge base articles

• Network documentation
• Operations documentation
• Standard changes

Monitoring & event management

• Target picture
• Central monitoring concept
• Event management process
• Alerting strategy
• Escalation model

Systems

• Cisco

• Palo Alto

• Fortinet

• Rubrik

• Veeam

• Matrix42

• Azure

• Microsoft 365 Automation

• Ticket creation from monitoring

• Escalations

• Standard actions

Audit, compliance & information security

• ISO 27001 consulting
• TISAX consulting
• NIS2 preparation - consulting
• Audit-ready processes
• Documentation structure
• Evidence tracking in Matrix42

Roadmap

• 12-month roadmap
• Prioritization of all measures
• Quick wins
• Medium-term projects
• Long-term target picture
• Documentation

• Responsible for introducing agile methods within the Interface & Integration Team (2 Scrum Teams)
• Planning and delivering trainings in agile methods
• Facilitating workshops and Scrum events
• Removing impediments
• Increasing sprint performance

• 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

• 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

• 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

• Created a multi-agentic system supported by a knowledge graph to automate drafting of research papers
• Used multiple experts (OpenAI models) collaborating during document drafting
• Extracted useful information from the knowledge graph
• Technologies: LangChain, LangGraph, Smolagents, LlamaIndex, dspy
• Infrastructure: Terraform and GitHub Actions (CI/CD) on AWS
• Deployed initial application as a Streamlit app

• 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).

Leading the end-to-end development of Edge AI-powered smart glasses for visually impaired individuals, aligning product vision with user needs and managing a cross-functional team of data scientists, Android developers, AWS engineers, and hardware specialists.

• Defined product roadmap for Edge AI smart glasses and MVP features through user research, stakeholder interviews, and competitive analysis, ensuring accessibility and real-world usability.
• Developed and validated a PoC for AI-driven cancer cell identification in PET/CT scans, collaborating with medical experts to optimize diagnostic accuracy and clinical relevance.
• Built and scaled a multidisciplinary team of 75+ engineers, interns, and designers across Germany and India, driving cross-border collaboration and iterative prototyping.
• Established strategic partnerships with NGOs, healthcare providers, and advocacy groups to embed inclusivity and patient feedback into product design.
• Drove hands-on hardware-software integration using Raspberry Pi and Jetson Nano of AI models. Initiated and nurtured relationships with suppliers, manufacturers, and ecosystem players to build scalable go-to-market plans.
• Owned critical product decisions, from prototype development to funding strategy, applying a data-informed and impact-driven mindset.
• Fostered a learning-focused culture by facilitating brainstorming sessions and continuous feedback loops between engineering and product.

Achievements:

• Public speaker: Auto.Ai 2025 (Berlin), Wearable technologies 2025 (Munich and Bangalore), MEDICA 2024 (Dusseldorf)
• WMF, Bologna, Italy (June 2024): Only AI startup to be selected from Germany as EBV hero to represent sprhava on global platform
• Medica, Germany (2024): Delivered a speech on AI smart glasses in world's largest Healthcare event.
• Wearable Technologies, Bengaluru, India (Dec 2024): I was a speaker presenting sprhava and its product.
• Venturise Global Challenge (GIM 2025, Bengaluru Palace, Karnataka): sprhava was selected as one of the 16 top startups (ESDM) to present on this global platform
• Wearable Technologies Conference 2025 EUROPE, Munich, Germany (May 2025): Delivered a talk on Edge AI at the Europe's biggest wearable tech event.
• InsurNext Köln, Germany (2025): sprhava was honoured with a booth from Cologne administration.

• Developed & maintained customer analytical reporting solutions to identify revenue trends, performance drivers, risks & optimization opportunities to ensure data driven decision making across Sales, Finance, Product, Data Engineering & Controlling.
• Defined & analyzed customer trends & performance metrics to identify root causes behind variances, anomalies & emerging risks across business domains to deliver actionable recommendations.
• Developed & owned analytical data models & reporting layers to ensure scalability, performance & analytical robustness to support executive & operational reporting across business domains.
• Planned, tracked & executed projects by ensuring adherence to timelines, data accuracy, consistency, deliverables, reliability & data quality standards through rigorous validation & reconciliation processes.
• Raised the analytical maturity by formalizing analytical workflows, documenting data processes & standard operating procedures (SOPs) & conducting training sessions to drive adoption of self-service analytics & embed a data driven culture across operational and business teams.
• Took ownership of the end-to-end lifecycle roadmap from requirement gathering, collection, transformation, developing robust business logics to data storytelling & stakeholder delivery.
• Converted complexity into structured clarity by translating requirements & business processes into analytical recommendations to ensure alignment between non-technical & technical stakeholders.
• Conducted advanced SQL based analysis of complex business datasets to uncover trends, correlations & performance improvement opportunities.
• Drove process automation & efficiency improvements by leveraging Python, SQL optimization & AI assisted tools to reduce processing time & increase reliability across analytical & operational workflows.
• Standardized KPI definitions & reporting logic to ensure consistency & trust across reporting solutions.
• Developed process monitoring dashboards & analyses to identify inefficiencies, bottlenecks & compliance deviations across end-to-end business processes to derive actionable recommendations for process improvement & automation.

• Set up Azure Kubernetes clusters (AKS) with network policies, security groups, and RBAC
• Developed Terraform-based infrastructure as code for secure, reproducible deployments in the BMW Azure cloud
• Hardened CI/CD pipelines using Jenkins, SonarQube, Fortify SSC, and Contrast AST
• Integrated SAP BTP/Kyma and ServiceNow GRC

• Installation, maintenance and regular upgrade of the DB systems with 19x
• Use of OPatch and RU 19.27 on RHEL 8x, SLES 15.x and Windows
• DBA for DACH and European customers in production and last test environments (e.g. Konrad Adenauer) as well as others in CDB
• Processes via Confluence
• Support in operations for customers SR via Metalink