Serverless Experts in Munich

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).

Omar A.

Engineering Leader · AI & Full-Stack Systems · Ex-Founder & CEO

Last position:

Senior Fullstack AI Engineer (Team Lead – B2C Platform) at mama health

Partner directly with C-level leadership (CEO, CAIO, CTO) on architecture, OKR strategy, and cross-team roadmap prioritization, translating strategic goals into structured engineering requirements.
Surfaced and mapped technical debt across the entire organization with C-level leadership and co-defined a prioritized remediation strategy, balancing debt paydown against feature delivery.
Led code reviews and technical standards across the team, fostering a mentor-first environment with two-way feedback dialogue — pairing on complex pipeline work and unblocking junior engineers on async architecture patterns.
Re-architected the AI companion's core processing pipeline from synchronous to asynchronous with a queue-based worker architecture, enabling horizontal scalability and cutting upload processing time ~4x (from ~22s to 5–10s) while improving response accuracy.
Designed an AI-driven document intelligence workflow with automatic multi-document classification, per-document summarization, and relevance guardrails for the patient care journey.
Built a unified patient memory system (short- and long-term context) bridging the document vault and chatbot into a single bidirectional, context-aware platform.

Luigi C.

Full Stack & AI Engineer

Last position:

Senior Full Stack Developer at Almuc Advertising GmbH

Applied modern JavaScript features, including ES6+ syntax, destructuring, modules, array methods, and asynchronous programming, to develop efficient and readable frontend code.
Implemented frontend integration patterns for PHP and Laravel backend services, connecting React interfaces to REST APIs and handling structured JSON data.
Developed interactive, functional component-based frontend applications using React Hooks, JavaScript, and TypeScript, alongside backend applications and RESTful APIs using Python, Django, Flask, and FastAPI, focusing on reusable components, modular architecture, maintainability, and efficient data processing.
Maintained a dynamic portfolio platform using PHP, Laravel, Livewire, and MySQL, implementing end-to-end functionality across frontend interfaces, backend services, and database operations.
Validated server-side request handling, routing, middleware, and form validation using Laravel features to support reliable application workflows and consistent data processing.
Designed and managed relational database structures using MySQL and Laravel Eloquent ORM, implementing data models, relationships, database migrations, and efficient queries for content management.
AI-powered applications integrating Large Language Models (LLMs) through external APIs and locally hosted models, exploring practical use cases for conversational AI, intelligent automation, and AI-assisted workflows.
Built local AI applications and developer tools for running, configuring, and interacting with locally hosted language models, gaining experience with local inference, model integration, and privacy-conscious AI workflows.
Explored Retrieval-Augmented Generation (RAG) approaches to combine language models with external knowledge sources, enabling context-aware responses based on relevant application data and retrieved information.
Integrated AI services with backend APIs and application components, developing reusable modules for model communication, response processing, error handling, and data exchange.

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

Siegfried-Thor B.

AI Risk Auditor for Enterprise CMS | AEM / AEMaaCS Architect | AI Governance (EU AI Act · NIST AI RMF · ISO/IEC 42001) | Web &

Grasbrunn

Last position:

Independent AI Risk Auditor & Software Architecture Reviewer at Confidential Client

Conducted an independent technical assessment of an AI-enabled software platform, combining AI risk auditing, cybersecurity analysis and software architecture review. Reviewed application components, APIs, data flows, LLM integrations, retrieval-augmented generation, multi-agent orchestration, authentication, tenant isolation, dependency vulnerabilities, AI governance and model lifecycle controls. Authored a technical audit report with a structured risk register, source-code evidence, regulatory references, corrective actions, AI system inventory, risk classification, remediation roadmap and Key Risk Indicators.

Technologies: TypeScript, JavaScript, React, Next.js, Node.js, REST APIs, MongoDB, Redis, AWS serverless services, OAuth 2.0 / OpenID Connect, LLM APIs, RAG, vector search, multi-agent orchestration, Model Context Protocol, C4 architecture modelling, threat modelling, NIST AI RMF, ISO/IEC 42001, ISO/IEC 23894, OWASP guidance, MITRE ATLAS

Vitaliy R.

DevOps GitOps (temp)

Puchheim

Last position:

DevOps GitOps (temp) at Signal Iduna

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.

Umut T.

Senior Product Engineer | Frontend, Full-Stack & Design | React & React Native

München

Last position:

App Engineer at Tonies SE

Worked as an App Engineer as part of the IoT Control team in a React Native Stack.

Implementing new Components based on the Tonies Design System.
Improving, extending and maintaining Tonies Design System
Adding new features like Volume and Playback Control of the Tonies
Fixed connection issues via bluetooth and wifi
Removing technical debt and polishing for an upcoming big software and hardware release
Improving accessibility by optimizing screens and components for usage via screen readers.
Wrote lots of automated tests to ensure app quality is always stellar.
Weekly app releases in a high volume workload environment

Results & Impact:

Improved app stability and connection reliability ahead of a major software and hardware release
Raised overall app quality through a significantly broader automated test suite, catching regressions before they reached users
Made the app usable with screen readers, improving accessibility for a wider audience
Sustained a weekly release rhythm in a high-volume production environment without quality regressions

Technologies: react-native, expo, typescript, react-19, react-compiler, react-navigation, zustand, @tanstack/react-query, i18next, figma, app-store, play-store, sentry, bitrise, config-cat, poeditor

Hardeep B.

Sr. Data Engineer

Last position:

Sr. Data Engineer at Charles Schwab Bank

Designed and implemented end-to-end data pipelines (batch & streaming) using Python, SQL, and Apache Spark, Databricks on AWS reducing ETL latency by 40%.
Developed serverless event-driven ingestion pipelines using AWS Lambda and SQS, ensuring real-time data availability for downstream analytics.
Leveraged Google Cloud Platform (GCP) services including BigQuery and Dataflow to manage cross-cloud data warehousing and analytics integration.
Expertise in DMS (CDC, Full Load) and Airflow for scalable data pipeline automation and orchestration.
Managed and customized data pipelines using Databricks, Airflow. Automation using Docker, Kubernetes, Terraform.
Automated data quality checks using dbt to modularize transformations and ensure production-grade data lineage, improving reliability by 30%.
Collaborated with compliance teams to ensure GDPR and SOC2 alignment. Mentored junior engineers and contributed to architecture refactoring for scalability.
Created and maintained dashboards in Power BI to provide actionable insights.

Piotr K.

Senior Software Engineer

Last position:

Senior Software Engineer at On

Built middleware service integrating EDI providers and marketplace partners with Microsoft Dynamics 365 to receive sales orders and communicate shipments, invoices, inventory, and price catalogues
Utilized a mixture of REST APIs and event-driven data processing pipelines

Alexandre S.

Cloud Engineer

München

Last position:

Cloud Engineer at Dectris AG

Build a scalable multi-region backend service in AWS to serve remote desktop virtual machines for scientific analysis
Stack: AWS, GitHub, Terraform, Python, Rust
Built and defined the core infrastructure of the backend system
Defined and coded the virtual machines provisioning supporting Ubuntu and Rocky Linux desktop setups
Programmed the API service running in ECS to manage virtual machines and build custom Docker images for users

Sara Z.

Data Analyst / Analytics Engineer

Last position:

Data Analyst / Analytics Engineer at IDG Tech Media GmbH

Designed, built, and maintained scalable ETL/ELT data pipelines using Python, SQL, REST APIs, AWS Lambda, S3, PostgreSQL RDS, EventBridge, CloudWatch, Docker, Apache Airflow, and BigQuery – integrating data from GA4, Google Ads, Meta Ads, CMS, CRM, newsletters, events, and B2C ordering systems into analytics-ready datasets.
Built a cross-brand lakehouse architecture from AWS to BigQuery – transforming raw JSON/CSV data into structured, partitioned, and reusable reporting layers with staging, intermediate, canonical, and mart models.
Designed relational and dimensional data models: 3NF staging models, star schemas, fact tables, dimension tables, daily KPI aggregates, and dashboard-optimized marts for marketing, content, subscription, event, CRM, and revenue analysis.
Implemented production-grade data quality and pipeline reliability features: incremental loads, idempotent upserts, deduplication, schema validation, row matching, null checks, anomaly detection, freshness monitoring, logging, retries, and error alerts.
Automated cross-brand reporting processes and data products – pipelines for 73 newsletter campaigns, 31 lead list syncs, 52 event partner reports, and a 500K-record company matching pipeline; reduced manual data preparation by approx. 70% and increased analyst productivity by approx. 30%.

Christian S.

Data-Scientist/AI Engineer

Ismaning

Last position:

Data-Scientist/AI Engineer at The Marcom Engine GmbH & Co. KG

Concept creation and implementing AI Agents in AWS Cloud
Continuously alignment with stakeholders
Collaborate with DevOps
Technologies: Git, CI/CD (GitHub Actions), Python/ML, Streamlit, Deno/typescript, AWS SAM, AWS Bedrock, AWS Lambda, AWS Dynamo DB, AWS S3, AWS Event Bridge etc.

Enis S.

Software Developer

Last position:

Software Developer at 50Hertz Transmission GmbH

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.

Roxana G.

Freelance Senior Frontend Developer