Helm Experts in Munich

• 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).

• Adjusting, optimizing, configuring, and administering a multi-stage GitLab instance with over 250 users
• Building, adjusting, expanding, and optimizing infrastructure, configuration, and monitoring
• Providing services and handing them over to production
• System environment: DependencyTrack, GitLab, Grafana, Hedgedoc, Kubernetes, OAuth2 Proxy, Openstack, Prometheus, Syseleven

• 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

• Responsible for Openshift/Kubernetes on-prem administration and developer support.
• Developed URP infrastructure automation with Python, Ansible, Kustomize and ArgoCD, Argo Workflow/Events stack.
• Wrote smoke and load tests for URP infrastructure utilizing Python, Kustomize and ApplicationSets.
• Helped to set up and deploy URP infrastructure in Google Cloud, GKE.
• Set up monitoring for URP and ArgoCD stack with Splunk Cloud.
• Performed system administration tasks across RedHat Linux, Kubernetes/Openshift, ArgoCD, GitLab, Bitbucket Enterprise, Kafka and MongoDB.

• Building an independent enterprise IT setup — cloud strategy, network, AWS landing zone, security requirements, contract negotiations.
• Migration of all applications; avoiding high contractual penalties for the client.
• Onboarding and coordination o...

• Participated in the gradual modernization of components into cloud-native 12-factor applications.
• Worked closely with the business operations team to eliminate manual processes and resolve several performance bottlenecks.
• Designed and implemented a CI/CD pipeline to increase developer productivity, enforce quality and security checks, and automate product delivery.
• Migrated several components into the OpenShift Kubernetes cluster.
• Built a monitoring stack from scratch with Prometheus and Grafana to monitor services running in OpenShift.
• Developed dashboards in both Grafana and Splunk for operational transparency.
• Implemented an OIDC/OAuth2-based single sign-on (SSO) solution with Keycloak to secure multiple applications.
• Technologies: Java, Spring, Quarkus, Kafka, MySQL, Cassandra, Redis, Spring Data, Hibernate, Docker, Kubernetes, OpenShift, Keycloak, OIDC, OAuth2, Helm, Prometheus, Grafana, Splunk, Spark.

I have worked across multiple international client projects, holding senior roles including Software Architect, Senior Software Developer, Technical Lead, and Lead Backend & DevOps Engineer. My experience spans complex enterprise environments in banking, financial services, telecommunications, engineering, and automotive domains, supporting organisations such as UniCredit Bank, Telefónica O2, and BMW.

At UniCredit Bank, within the Securities Domain Transformation program, I led the modernisation of legacy monolithic systems into cloud-native Spring Boot microservices and an Angular frontend deployed on Google Cloud Platform. Beyond implementation, I was responsible for defining the target architecture, producing system architecture diagrams and sequence diagrams, and preparing API contract documentation for clients. I designed RESTful APIs and integrated Apigee for secure and reusable cross-project service consumption of APIs. I architected Kubernetes-based deployments using Helm. CI/CD pipelines were built with Jenkins, automating code analysis using Sonar, as well as testing and deployment stages. Defining clean coding principles for the project, conducting regular code reviews, and mentoring junior developers were also among my tasks at UniCredit.

At Telefónica O2, I led the transformation of a legacy call centre desktop application into a cloud-native microservices and micro-frontend solution. I actively contributed to the platform architecture, creating system architecture diagrams, component diagrams, architecture documentation, and ADRs for future references. I improved the performance and scalability of the services. I optimised AWS infrastructure costs, particularly by minimising the use of DynamoDB and reusing test environments effectively. Observability was implemented using Prometheus, Grafana, CloudWatch, and Splunk dashboards. CI/CD pipelines were delivered using GitLab, Docker, Kubernetes, and AWS. Conducted techinical sessions for teams.

• 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.

• 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

• 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

• Act as interface between business, IT demand management and IT architecture, analyzing functional and technical requirements in Salesforce.
• Create requirements and specification documents together with the business unit and develop solution concepts.
• Define, control implementation and acceptance of epics and user stories.
• Advise business units on optimization potentials and develop functional solution concepts.
• Support project management and product owners in scope analysis and ticket creation.
• Develop identity management solutions and deletion concepts in the Salesforce environment.
• Define and implement DWH reports for analysis of business areas and process optimizations.
• Document and optimize functional process workflows.
• Tools and technologies: Miro, Salesforce, Draw.io, Atlassian Stack (Confluence, JIRA), Figma, Enterprise Architect, MS Teams, Slack, Zoom, SCRUM, agile project management.

• Creation of test concepts
• Development of test processes
• Coordination of test case development: stress tests, functional tests, performance tests, high data rate tests, integration tests, etc.
• Hardware testing: FPGA, RF
• Test automation and regression testing
• Ensuring 24/7 operation of the test system
• Analysis & reporting
• Regular coordination of the test team, meetings with other stakeholders
• Communication and coordination with stakeholders and project managers

• Architected and implemented an enterprise-grade AI Agent Platform leveraging Retrieval Augmented Generation (RAG) architecture to enhance clinical data insights.
• Established robust CI/CD pipelines for LLM applications using CDK and Jenkins, significantly reducing deployment times.
• Implemented comprehensive observability solutions that increased agent reliability across pharmaceutical environments.
• Designed scalable AI workflows with advanced orchestration that optimized context handling for enterprise data sources.
• Technologies: AI Agents (LangChain, LangGraph, Bedrock, Smolagents, Streamlit); LLM Operations (Tracing, Testing, Evaluation, LangSmith, LangFuse); Infrastructure-As-Code (AWS CDK, Terraform, Typescript, Jenkins); Vectors, Embeddings, RAG (OpenSearch, pgvector, PDF Extraction)

• Operations and extensions of Azure DevOps pipelines

• Operations and extensions of AWS services

• Citrix (Windows 10, Bitwarden)

• AWS: ECR, EKS, CloudFront CDN, Route 53, VPC peering and CNI upgrade, Atlas MongoDB, S3 buckets, static website hosting

• Azure: build and deploy with DevOps pipelines