Kubeflow Experts in Munich
, matched in minutes from over 15,000 CVsHire experts who build reproducible machine learning pipelines, operate training workloads on Kubernetes, and connect Kubeflow with cloud data and model services. FRATCH finds the right vetted, available freelancer through fast, precise AI matching.
Meet FRATCH Experts in Munich, who have recently used Kubeflow
Michael N.
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.
Philipp G.
Last position:
Data Scientist & ML Engineer at Data-Science Factory GmbH
- 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
Thomas H.
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).
Serge K.
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
Martin M.
Last position:
Product Owner AI Learning Platform at B2B Tech Scale-Up
- Agile development of a multimodal analysis platform for training materials (video, audio, documents) using Scrum
- Extraction of context-relevant content based on user profiles & competency dimensions
- Personalized delivery of learning content to improve sales performance
- Close coordination with sales teams & stakeholders to validate features
- Use of Gemini, Whisper, Python & JavaScript, deployment on AWS, Perl for scripting data imports
- Integration into existing tools & CRM systems for smooth adoption
- Technologies used: Python, OpenAI, DB tech such as PostgreSQL, CI/CD for AirFlow DAGs, FastAPI
Stephan S.
Last position:
Senior Data/ML Consultant & Technical Lead at Jolin.io
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)
Himanshu N.
Last position:
Principal (Data Scientist/Data Engineer/Gen AI Engineer) at Marktguru Deutschland GmbH
Architected an agentic, real-time offer orchestration engine where specialized agents (retrieval, pricing/optimization, and policy/guardrails) coordinate to personalise promotions across customer touchpoints using RAG with FAISS over Delta Lake and low-latency Databricks Model Serving. Collaborated with product managers and commercial stakeholders to shape the roadmap and evaluate emerging agent patterns for production.
Designed an agent-based data quality service that orchestrates schema detection, entity normalization, and validator/exception-handling agents to clean multi-retailer SKU feeds at scale. Wrapped model calls in PySpark UDFs for distributed inference, automated via Databricks Workflows and CI/CD.
Developed a multimodal, agentic extraction pipeline where vision, parsing, and compliance agents collaborate to derive brand, packaging, and volume from scanned images using Claude 3 Sonnet with Swin Transformer encoders. Orchestrated via Azure Event Hub with outputs persisted to Delta Lake.
Implemented a GS1 taxonomy classification service built around cooperating agents for inference, drift monitoring, and auto-retraining governance using Falcon 180B (LoRA-tuned) with a batch pipeline on Databricks.
Created a hybrid agent workflow where a retrieval agent surfaces candidate matches via embeddings and a reasoning/verification agent (Mixtral 8x7B) adjudicates receipt-to-SKU alignment, integrated into a streaming Databricks pipeline.
Built a multimodal attribute inference pipeline structured as cooperating vision-language, rules/consistency, and compliance agents to fill NutriScore, nutrition fields, and packaging types from names and images using LLaMA 3-8B with CLIP embeddings.
Developed a GenAI-powered orchestration system that ingests recipes from multiple websites, parses ingredients through structured extraction agents, and dynamically links them to real-time retailer offers via tagging, semantic reasoning, and business-rule agents.
Discover over 15,000 top freelancers
Statistics of experts using Kubeflow
Aggregated from the professional profiles of matched freelancers.
Experience
17 years
Position duration
2.5 years
Positions per freelancer
11
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Education, Banking and Finance
Certification focus areas
Business Intelligence, Information Technology, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
33%
Certifications per freelancer
4
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Munich are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts in Munich using Kubeflow
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Kubeflow experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (100%)
- Education (57%)
- Banking and Finance (57%)
- Manufacturing (57%)
- Professional Services (57%)
- Automotive (43%)
- Insurance (43%)
- Transportation (43%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Kubeflow does
Kubeflow is an open-source platform for running machine learning workflows on Kubernetes. It helps teams package experiments, schedule training, track model work and move reliable pipelines toward production. Its components support both data science and platform operations.
Core building blocks
Kubeflow combines notebooks, training operators, pipelines and model serving tools. Kubeflow Pipelines defines repeatable workflows, while Katib supports hyperparameter tuning and metadata services help teams record experiments. Strong implementations also connect storage, identity, monitoring and CI/CD.
- Design reproducible training and inference pipelines
- Run distributed workloads with Kubernetes resources
- Serve models through scalable endpoints
- Connect experiments with registries and observability
Where it runs
Companies use Kubeflow for computer vision, language models, recommendation systems, forecasting and fraud detection. It can support research environments, internal AI products and regulated production systems across manufacturing, mobility, finance, healthcare and media. Teams may run it on managed Kubernetes or their own clusters.
When specialists help
Freelance expertise is useful when a team needs to establish a machine learning platform without slowing product delivery. Specialists can migrate scattered notebooks into pipelines, improve training reliability, set up model serving or resolve difficult Kubernetes and cloud integration issues.
- A proof of concept must become a repeatable workflow
- Training jobs consume shared cluster resources unpredictably
- Models need controlled promotion and rollback
- Data, infrastructure and machine learning teams lack a shared process
Skills that matter
Effective Kubeflow professionals understand Kubernetes, containers, Python and distributed computing. They should also work confidently with cloud storage, workflow orchestration, secrets, networking, GPU scheduling and monitoring. Experience with MLflow, Spark, Argo or cloud-native registries can be valuable when these tools surround the platform.
Choosing the right expert
Look for evidence of production pipelines, not only notebook experiments. Ask how the specialist handles pipeline versioning, reproducibility, resource isolation, model validation, security and incident recovery. For Munich teams, remote collaboration can work well when documentation and handovers are strong; on-site work may help during cluster migrations or workshops, with German or English agreed in advance.
Frequently asked questions
Questions about Kubeflow? Start with the answers below.
Kubeflow is used to develop, train, deploy and monitor machine learning workflows on Kubernetes. It gives teams reusable pipelines and operational controls for moving models from experiments into dependable services.
Kubeflow is a broader Kubernetes-native platform for orchestrating machine learning workloads, while MLflow focuses strongly on experiment tracking, model packaging and lifecycle management. They can be used together rather than treated as direct substitutes.
Kubeflow work usually requires Kubernetes, Docker, Python and cloud infrastructure knowledge. Helpful adjacent skills include Argo Workflows, MLflow, Spark, Terraform, GPU scheduling, observability and secure service networking.
Kubeflow expertise should include production delivery, not just configuring a demonstration cluster. A capable freelancer can design maintainable pipelines, control compute costs, secure access, document operations and explain trade-offs to data and platform teams.
Kubeflow can suit a small team when it needs repeatable workflows, shared infrastructure or a clear path to production. Its operational overhead may be too high for a few simple experiments, where managed services or lighter tooling can be more practical.
Kubeflow projects are often suitable for remote collaboration because configuration, pipelines and infrastructure can be reviewed online. On-site sessions in Munich may still help with architecture workshops, access constraints, cluster migrations or team training.
Kubeflow quality is best assessed through specific examples of pipeline design, deployment incidents and production improvements. Ask the freelancer to explain reproducibility, resource management, model promotion, observability and recovery without relying on vague platform claims.
Kubeflow pipelines become reliable when steps are versioned, inputs are tracked and failures can be retried safely. Strong practices also include automated validation, controlled artefact storage, resource limits, access management and monitoring for both infrastructure and model behaviour.
The average hourly rate of freelancers in Munich, Germany who have used Kubeflow in their recent projects is 107 €, which corresponds to a daily rate of about 854 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Kubeflow in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 33% hold a doctorate.
On average, freelancers in Munich, Germany who have used Kubeflow in their recent projects have 17 years of professional experience, with a single engagement typically lasting around 2.5 years.
The most common languages among freelancers in Munich, Germany who have used Kubeflow in their recent projects are German (100%), English (100%), and French (43%).
The most common industries among freelancers in Munich, Germany who have used Kubeflow in their recent projects are Information Technology (100%), Education (57%), and Banking and Finance (57%).
The most common business areas among freelancers in Munich, Germany who have used Kubeflow in their recent projects are Information Technology (100%), Product Development (100%), and Business Intelligence (86%).
Main locations of FRATCH Experts, who have recently used Kubeflow
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Countries:
Berlin Hamburg 
Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
