Kubeflow Experts in Munich
matched in minutes by AIHire experts who create Kubeflow Pipelines, deploy models with KServe and manage training workloads on Kubernetes. Get precise access to vetted, available freelancers who fit your technical needs quickly.
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:
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
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.
Martin M.
Last position:
Product Owner AI Learning Platform at B2B Tech Scale-Up
- Agile setup 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 boost 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 like PostgreSQL, CI/CD for Airflow DAGs, FastAPI
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 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Munich are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge 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 19 Sep 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 developing, training and deploying machine learning workflows on Kubernetes. It brings notebooks, experiment tracking, pipeline orchestration and model serving into a container-based environment. Teams use it to turn repeatable data science work into managed production processes.
Core Components
Kubeflow includes tools such as Kubeflow Pipelines, Katib for hyperparameter tuning, Jupyter notebooks and KServe for inference. Its components connect with Kubernetes resources, container registries, object storage and identity systems. Strong specialists understand how these parts work together rather than treating Kubeflow as a standalone application.
Typical Projects
- Create reproducible pipelines for data preparation, training, validation and deployment
- Run distributed training with frameworks such as TensorFlow, PyTorch or XGBoost
- Serve models through scalable, versioned inference endpoints
- Establish experiment tracking, approval steps and rollback processes
- Operate machine learning workloads across cloud or private Kubernetes clusters
Kubeflow supports use cases from computer vision and natural language processing to forecasting, recommendation and industrial analytics.
When Companies Need Help
Companies usually bring in freelance Kubeflow expertise when machine learning work must move from notebooks into dependable operations. Typical signals include fragile manual handovers, slow retraining, unclear model lineage or Kubernetes clusters that are not prepared for data-intensive workloads. In Munich, specialists may support local teams on site or collaborate remotely with distributed product and data groups.
Skills Around Kubeflow
Effective work with Kubeflow requires more than knowledge of its user interface. Professionals should be comfortable with Kubernetes, Docker, Python, Linux, cloud infrastructure and CI/CD practices. They may also work with MLflow, Airflow, Spark, Argo, Prometheus, Grafana and cloud services for storage, identity and compute. Knowledge of data governance and secure access is valuable when models handle sensitive business data.
What Strong Specialists Deliver
Strong professionals design pipelines that are modular, observable and easy to reproduce. They define resource limits, secrets management, artifact storage and promotion rules instead of leaving operational details implicit. They also test failure handling, document deployment decisions and make workflows understandable to data science and platform teams. The best fit can explain trade-offs between managed services and self-hosted Kubeflow while aligning the solution with the company’s existing Kubernetes environment.
Frequently asked questions
Questions about Kubeflow? Start with the answers below.
Kubeflow is used to build, run and maintain machine learning workflows on Kubernetes. Companies use it for repeatable training pipelines, experiment management, distributed workloads and production model serving.
Kubeflow offers portability and control because teams can run its components on their own Kubernetes infrastructure or across cloud environments. Managed services can reduce operational work, but they may provide less control over deployment patterns, integrations and infrastructure choices.
A strong Kubeflow specialist should understand Kubernetes, containers, Python and cloud infrastructure. Experience with CI/CD, observability, data pipelines, model registries and frameworks such as PyTorch or TensorFlow is also useful.
The right Kubeflow experience depends on the scope. A pipeline prototype may need focused workflow and container knowledge, while a production platform requires expertise in Kubernetes operations, security, reliability, model serving and team enablement.
Yes, Kubeflow projects are often suitable for remote collaboration because infrastructure, pipeline definitions and deployment workflows can be reviewed online. On-site work in Munich can still help when specialists need to align closely with platform, data and compliance teams.
Ask a Kubeflow specialist to explain a complete workflow from data preparation through deployment and monitoring. Look for clear decisions about reproducibility, resource management, security, failure recovery and how the design fits the existing Kubernetes environment.
Kubeflow overlaps with parts of Airflow and MLflow but serves a different purpose. Kubeflow Pipelines focuses on machine learning workflow orchestration, while Airflow is a general data workflow tool and MLflow commonly supports experiment tracking, model packaging and registry functions.
Before joining a Kubeflow project, freelancers should clarify the Kubernetes distribution, cloud or on-premises setup, pipeline engine, model serving approach and ownership of operations. They should also confirm expectations for documentation, on-call support, security reviews and collaboration with data science teams.
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
