Grafana Expert in Munich

Karen M.

Senior .NET Backend Engineer | Applied AI | Agentic Systems, RAG & Distributed Architecture

Last position:

Personal AI Engineering Project — Croky AI at Crocky AI

Product:

Built a production-ready AI platform for generating brand-aware marketing images and videos from product data, user requirements, and uploaded media.
Own the platform architecture, technical roadmap, API design, security, deployment workflow, operational reliability, and model-provider strategy.
Developed the core platform in .NET and built supporting AI and workflow prototypes in Python, applying language-independent API contracts and structured interfaces between services and model providers.
Implemented reliable background processing with RabbitMQ, persisted workflow state, idempotent handling, retries, failure recovery, logging, secure storage, authorization, and credit accounting.
Made pragmatic build-versus-buy and model-routing decisions based on reliability, latency, cost, and maintainability rather than novelty.

Agent Orchestration & RAG Systems

Built and compared agent workflows using Microsoft Agent Framework, LangGraph, and LangChain, including tool use, conditional routing, clarification steps, state management, and hand-offs between agents.
Implemented reusable .NET components for agents, prompts, tools, model providers, structured responses, and retrieval with pyvector, making it easier to change AI providers without rewriting the core workflow.

Ljubomir O.

Senior Test Automation Engineer | QA Engineer

München

Last position:

Senior Software Test Engineer at Keil KTM GmbH

Temporary employment

System black-box integration tests (BBIT, IVVQ): Execution of regression, release, confirmation and conformity tests for safety-critical braking control units in the railway 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 conformity with 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 and 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 adjustment 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): Work 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 levels (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

Ales L.

Senior DevOps Consultant (Freelance)

Last position:

Senior DevOps Consultant (Freelance) at European Union Agency (via IBM)

Worked as freelance Senior DevOps Consultant on-site for IBM at a European Union Agency, operating in a highly secure, air-gapped environment managing classified systems.
Led automation and DevOps initiatives for a large-scale OpenShift platform (>400 nodes), driving deployment efficiency, GitOps adoption, and operational automation using Ansible, Python, and Bash while ensuring compliance with security requirements.
Spearheaded automation of release and deployment workflows in a private cloud environment hosting 400+ OpenShift nodes, significantly improving deployment speed and reliability.
Migrated existing playbooks, roles, and templates from Ansible Tower to Ansible Automation Platform (AAP), ensuring full compliance with fully-qualified collection names (FQCN) and preparing custom Execution Environments (EE) for containerized automation.
Implemented GitOps Agent for AAP Controller Configuration as Code, enabling automated synchronization (CRUD) of Ansible Controller objects based on repository-stored configuration definitions using GitHub webhooks.
Designed and automated complex multi-step operational workflows including environment cleanup, Helix cluster component re-creation, Kafka topic management, and OpenShift object lifecycle management across ~100 environments.
Achieved a reduction of multi-day manual operations to under a few hours through automation improvements spanning multiple AAP clusters and OpenShift environments.
Integrated Ansible Automation Platform with Thycotic (Delinea) Secret Server via lookup plugin to enhance secure credential management in automated processes.
Managed deployment tasks, platform troubleshooting, and Istio network configurations while adhering to stringent EU PSC security and compliance standards.
Collaborated with infrastructure and application teams to refine deployment procedures, develop naming conventions, and continuously improve automation coverage in an air-gapped, classified environment.

Michael N.

Senior ML Engineer | AI Engineer | Problem Solver

Eichenau

Last position:

Senior AI Engineer | Forward Deployed Engineer at Tiefbau

Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.

Mirza K.

Agentic AI for a DeepResearch project

München

Last position:

Agentic Automation and a RAG system

This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.

Used: Python, RAG, LangGraph, LangChain, deepeval, MCP

Vicenco K.

Interim IT Team Lead / IT Service Management / IT Project Management / Solution Architect

Brunnthal

Last position:

ITSM Project Manager (self-employed)

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

Marco T.

Scrum Master

München

Last position:

Scrum Master at Siemens Energy

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

Markus B.

Software Development / Programming; Consulting; Project Management / Management / Organization / Coordination;

Last position:

Developer / DevOps, Consulting at A state authority in Baden-Württemberg / FSAS Technologies GmbH

Oracle Project Management and Automation

As part of the project, the operation of an Oracle infrastructure was automated by developing and enhancing Puppet modules and migrating existing Bash scripts to a standardized configuration management solution.

The aim of the project was to standardize and automate administrative processes, improve the maintainability of the Oracle platform, and increase the efficiency and traceability of infrastructure management through the use of Infrastructure as Code.

Analysis and migration of existing Bash shell scripts to a Puppet-based automation solution
Development and enhancement of Puppet modules for Oracle environments
Automation of installation, configuration and operational processes using Infrastructure as Code
Standardization and optimization of configuration management for Oracle systems
Creation and maintenance of technical and operational documentation
Planning, coordination and professional support during project implementation
Operation and automation of Oracle systems on x86-based Linux server platforms
Testing and quality assurance of the developed Puppet modules
Analysis and resolution of technical errors as part of the automation and migration
Close coordination with specialist departments and infrastructure teams to implement automation requirements

Technologies used: Puppet 7/8, SLES 15, Oracle 19, Oracle Grid, SVN, Windows Server, Confluence, VS Code, Ruby, Bash, Pass

Tamás E.

Senior Software Developer / Tech Lead

Last position:

Senior Software Developer / Tech Lead at NDA (defense / OSINT)

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

Ronald M.

DevOps Consultant

Last position:

DevOps Consultant at M.it services & systems GmbH

Adaptation, optimization, configuration, and administration of a multi-stage GitLab instance with over 250 users
Setup, adaptation, expansion, and optimization of infrastructure, configuration, and monitoring
Provisioning of services and handover to production
System environment: DependencyTrack, GitLab, Grafana, Hedgedoc, Kubernetes, Oauth2 Proxy, Openstack, Prometheus, Syseleven

Alexandru G.

Head of Cloud Infrastructure

Last position:

Principal Cloud DevOps Architect at BP

In my role as Senior Cloud DevOps Architect for BP, an oil and gas company, I had the mission to migrate the Electric Vehicle Charging platform of the EV Division from on-premises and Azure to AWS cloud, resulting in a hybrid multi-cloud, multi-tenant SaaS solution.

Deployment with Kubernetes for the application layer meant provisioning Kubernetes clusters managed by EKS and AKS, with a focus on integrating them into a multi-tenant environment. This integration was achieved by using Kubernetes namespaces and access controls to ensure data isolation and privacy enforcement.

In the database layer, we chose an RDS instance with PostgreSQL to support the backend infrastructure of our applications. Tenants shared the same RDS instance, but each had a dedicated schema.

To ingest near real-time data from physical charge points (CPOs), as IoT devices, via the OCPI protocol, we ran into significant delays with batch processing. As a result, we built a real-time streaming data pipeline using Apache Kafka, while prioritizing an event-driven architecture.

Led collaboration across multiple internal teams, external vendors, cloud providers, and on-site partners to integrate over five systems into a unified solution.

Achievements:

Successfully designed and implemented hybrid multi-cloud solutions, integrating multiple cloud platforms (AWS, Azure) with on-premises infrastructure, using Site-to-Site VPNs, Firewalls, and Load Balancing.
Led the migration of on-premises infrastructure to multi-cloud, multi-tenant infrastructure, resulting in 30% faster processing times.
Migrated workloads from VMware and Hyper-V environments to cloud-based VMs, leveraging cloud-native services to optimize performance, cost efficiency, and scalability.
Designed a multi-tenant Kubernetes platform leveraging the Kubernetes ecosystem, using Karpenter for dynamic EC2 node provisioning, KEDA for event-driven pod autoscaling (e.g., Kafka message lag), and Rancher for centralized monitoring of multiple clusters (EKS, AKS, or on-prem K8s), replacing Microsoft-centric Azure Arc management service.
Designed and implemented Python-based FastAPI microservices as part of the EV core-backend on AWS EKS application layer, powering data ingestion and customer analytics pipelines.
Developed asynchronous, event-driven APIs (Python-FastAPI) for real-time integration with CPOs, supporting OCPI 2.3 and OICP protocols.
Designed and implemented a secure, production-grade Azure Databricks platform using Terraform, ensuring scalability and cost efficiency.
Migrated on-premises ERP to a hybrid Dynamics 365 architecture with ERP hosted locally and CRM running in Azure, integrated via Azure Arc.
Automated CI/CD pipelines for Databricks notebooks and jobs using GitHub Actions & Databricks CLI, reducing deployment time. Reduced infrastructure provisioning time by 70% by automating cloud resource deployment with GitOps.
Ensured compliance with internal audit and data governance standards (GDPR) through OAuth2/OIDC-based authentication and fine-grained role-based access controls.
Developed a Zero Trust security model, enforcing least-privilege access and microsegmentation, enhancing security posture and compliance with GDPR and NIST.
Built interactive analytics dashboards in Amazon QuickSight, integrating data from S3 and Redshift to deliver real-time business insights and visualizations with embedded access for multi-tenant users.
Led cloud security assessments and full-lifecycle cybersecurity integration during M&A, covering AWS, Azure, IAM (Entra ID), and data protection, while aligning security posture with NIST, ISO 27001, and GDPR across hybrid and cloud-native environments.
Reduced cloud costs by 64% for a client's dev environment by implementing automated start/stop schedules for EC2 and RDS instances via AWS CDK with EventBridge Scheduler or AWS Systems Manager.

Tech stack:

Infrastructure as Code: Terraform, AWS CDK, Ansible.
Containers: Kubernetes on EKS, AKS, Docker.
Streaming Data Processing: Kafka to Confluent Cloud, after AWS MSK.
Frontend: TypeScript, React, NextJS, Hooks, Styled Components.
Backend: Python with FastAPI, also Node.js with NestJS.
Database: Aurora on PostgreSQL with TypeORM, RDS on SQL Server, Azure Databricks full setup and administration, ETL Pipelines.
CI/CD and GitOps: GitHub Actions, Azure DevOps, ArgoCD.
Monitoring and Observability: Prometheus and Grafana.
Virtualization: Hyper-V, VMware Cloud on AWS, Azure Migrate.
ERP Systems: Odoo, Microsoft Dynamics 365 Business Central on Azure, integrated with Azure Arc.
Networking: Site-to-Site VPNs, AWS Direct Connect, Azure ExpressRoute, Firewalls (AWS Network Firewall, Azure Firewall).
Security: IAM, NIST Framework, Zero Trust Security, AWS WAF, AWS Shield, GuardDuty.

Thomas H.

Senior MLOps, DevOps Engineer

Last position:

Senior MLOps, DevOps Engineer at Trianel Energy

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).

Anton L.

Senior Solution Engineer · Mobile Native, Server-side, Digital Identity/KYC, Computer Vision & AI

Baierbrunn

Last position:

Senior Solution Architect

Internal systems with regulatory and security requirements could not be used directly by AI tools for incident triage, troubleshooting, and documentation search. The goal was to securely integrate these systems through MCP while preserving existing acce...

Serge K.

Data Science & Machine Learning Operations

Last position:

Data Scientist & MLOps (Machine Learning Operations) at REWE Digital GmbH

It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment
Development of feature store and data synchronization between environments
Data science: evaluation of different forecasting models, including CatBoost, Contextual Gamma and Random Forest; implementation of training pipelines using Kubeflow on Vertex AI; scaling and development of an event-driven interface using Kafka as a forecasting service; evaluation of MLFlow for model tracking and model registry
Implementation of REST API using KeyCloak OAuth2 and OpenID (JWT)
Monitoring: implementation of dashboards with Grafana, Prometheus, Mimir, Streamlit and Loki to monitor prediction metrics, GCP and Snowflake costs for pipelines, and system metrics
Development of Copilot Agent Skills

Frank E.