MLflow Experts in Munich
for reliable machine learning delivery, matched in minutesHire experts who track experiments, package models and connect MLflow with cloud data platforms, Kubernetes or Databricks. FRATCH matches you quickly and precisely with vetted, available freelancers for your project.
Meet FRATCH Experts in Munich, who have recently used MLflow
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.
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
Vitaliy R.
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.
Stephan B.
Last position:
Freelance Data Scientist at Baier Data & AI Consulting
Caner K.
Last position:
Synthetic Medical Dataset (MedGym) at MedTank
- Generated synthetic datasets for CXR, mammography, and distal radius fracture detection using GANs and diffusion, creating >50k synthetic images for benchmarking.
- Ensured GDPR-compliant workflows and reproducibility, enabling dataset adoption for internal validation and academic collaboration.
- Project highlighted in MedTank’s internal R&D showcase as a flagship synthetic data initiative.
Nima N.
Last position:
Co founding LLM Engineer at LLM Ventures
- Co-founded an AI venture focused on building production-grade LLM applications and agentic systems
- Designed and implemented multi-agent AI workflows for financial and trading applications
- Developed LLM-powered copilot architectures for portfolio analysis, trade management, and personalized user coaching
- Built on-device and edge-deployed inference applications, optimizing models for low latency, privacy, and resource-constrained environments
- Led system architecture decisions across model selection, orchestration, state management, and deployment
Patrick U.
Last position:
Interim Management | Consulting & Implementation | Data Deletion in SAP at BSR (Berliner Stadtreinigung)
- Topics: Business Analysis, Data Privacy, Data Management, Stakeholder Management, Conceptualization
- This project focuses on developing and implementing a strategic approach for data deletion in SAP systems. The goal is to identify the relevant data and structures during system migration to ensure both data privacy and IT system efficiency. At the same time, downtime should be minimized and regulatory requirements met.
- Development of a comprehensive approach for data deletion in SAP systems, considering data privacy and business requirements.
- Ensuring efficient and structured data transfer to the new system.
- Optimizing system efficiency and reducing downtimes during migration.
- Creating functional and technical concepts to ensure compliant and sustainable data management.
- Topic preparation: Detailed study of the "data deletion" area to lay the foundation for a structured data migration.
- Definition of project structure: Setting roles, interfaces and the project's organizational structure.
- Regulatory requirements: Analysis of data privacy regulations and business requirements to define deletion criteria.
- Approach: Developing possible scenarios and methods for data cleansing and deletion.
- Deletion concepts: Creating functional and technical deletion concepts that structure the implementation and provide clear guidelines.
- Setting deletion criteria: Defining which data and structures to delete or transfer.
- Responsibilities: Clarifying responsibilities within the project team and among stakeholders.
- Analysis of ongoing activities: Identifying and collecting existing activities in the "data deletion" area.
- Effort, cost and timeline planning: Creating estimates for resources, effort and budget.
- Implementation initiatives: Developing and executing concrete measures to apply the defined deletion strategies.
- IT system efficiency: Analyzing the existing IT infrastructure to identify optimization potential for data deletion and transfer.
- Technology trends: Evaluating new technologies and tools that can support the data cleansing process.
- Cost-benefit analysis: Assessing the financial impact of data cleansing and the introduction of new solution approaches.
- Risk management: Identifying potential risks during implementation and developing appropriate mitigation measures.
- This project lays the foundation for a sustainable and compliant data transfer to a new SAP system. With a clear approach to data deletion, it meets data privacy requirements, reduces downtimes and increases the efficiency of the new system. The results and recommendations will help companies develop a future-proof data strategy that meets legal and business needs.
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)
Maziyar K.
Last position:
Data Engineer at MSD Germany
- 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
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
Mohamed S.
Last position:
Machine Learning Engineer (Part Time) at E.ON Digital Technology
- Designed and implemented an advanced, agentic RAG pipeline using LangChain and LangGraph for structured data extraction from PDFs, utilizing tools, state management, and OpenAI LLMs (GPT-4) to improve accuracy and handle complex document structures.
- Developed a Google AI agent for extraction of structured information from PDF documents and deployed the agent on Vertex AI.
- Architected data pipelines using Azure Data Factory and Databricks to ingest data from Azure Blob Storage, process it with PySpark, and load it into Azure SQL Database via Linked Services.
- Containerized AI agents and services using Docker for consistent local development and deployment.
- Utilized PySpark and Dask for database querying in coordination with Azure Blob Storage and Document Storage.
- Created a ReAct agent that extracts structured data from PDF documents using tools and integrating Azure Document Intelligence.
- Contributed to the CPO invoices validation check project using Databricks to find existing CDRs and calculate total valid costs.
- Developed a conversational AI agent (chatbot) with a FastAPI backend, integrating RAG for precise tariff extraction and deployed the service using Azure Container Apps.
- Tools used: Azure, Azure OpenAI, Azure Document Intelligence, Azure Blob Storage, Google ADK, Google Cloud, Vertex AI, Gemini, Databricks, LangChain, LlamaIndex Ollama, Docker, PySpark, Azure SQL, Azure Data Factory, Azure AI Agent, Microsoft SQL Server
Discover over 15,000 top freelancers
Statistics of experts using MLflow
Aggregated from the professional profiles of matched freelancers.
Experience
15 years (Germany: 12 years)
Position duration
1.7 years
Positions per freelancer
11 (Germany: 8)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Professional Services, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
90% (Germany: 77%)
Doctorate
30% (Germany: 20%)
Certifications per freelancer
3 (Germany: 2)
Most common languages
German, English, Italian
Speak two or more languages
92% (Germany: 96%)
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 MLflow
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.
MLflow 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 (92%)
- Professional Services (67%)
- Banking and Finance (58%)
- Insurance (58%)
- Education (50%)
- Manufacturing (50%)
- Retail (50%)
- Transportation (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What MLflow does
MLflow is an open-source platform for managing the machine learning lifecycle. It records experiments, parameters, metrics and artifacts, then helps teams package, register and deploy models consistently. Its components support repeatable work from exploratory training through production operations.
Core components
MLflow Tracking stores run data in a searchable backend while artifacts remain available for review and comparison. Projects define reproducible execution, Models standardizes packaging and serving, and the Model Registry supports versioning, promotion and ownership. Strong specialists also configure authentication, storage and environment separation.
Typical use cases
- Compare training runs across notebooks, scripts and automated pipelines
- Register models and manage transitions between testing and production
- Serve models through APIs or package them for batch inference
- Connect experiment tracking with Databricks, Kubernetes or cloud storage
MLflow fits teams that need a shared record of how a model was produced and why a particular version was released. It is useful for forecasting, recommendation, natural language, computer vision and other data-driven products.
Ecosystem and skills
MLflow commonly works alongside Python, scikit-learn, PyTorch, TensorFlow and Spark. Delivery may also involve Git, Docker, Kubernetes, Airflow, Kubeflow, data warehouses and cloud services. Professionals should understand model signatures, artifact stores, deployment targets, pipeline automation and the security of sensitive training data.
In Munich, companies may use MLflow within automotive, manufacturing, finance, healthcare or enterprise analytics environments. Local collaboration can be on-site, remote or hybrid, depending on data access and the need to coordinate with existing data and product teams.
When to bring in expertise
- Experiment records are inconsistent across teams or notebooks
- Models reach production without clear lineage or rollback paths
- A registry, serving setup or cloud integration needs careful design
- Training pipelines must become reproducible and easier to operate
Freelance expertise is valuable during platform selection, migration, production rollout and troubleshooting. A specialist can also define practical conventions so data science, infrastructure and product teams share the same workflow.
What strong specialists deliver
Look for hands-on evidence of tracking design, model packaging, registry governance and deployment automation. Strong professionals explain trade-offs between MLflow and adjacent tools, test integrations in realistic environments and document decisions clearly. They distinguish an experiment that works locally from a model lifecycle that remains observable, reproducible and maintainable in production.
Frequently asked questions
Everything clients usually want to know about MLflow, in one place.
MLflow is used to track machine learning experiments, store artifacts, package models and manage model versions. Companies use it to improve reproducibility and create a clearer path from training to deployment.
MLflow focuses on an open, modular lifecycle for tracking, packaging, registering and serving models. Kubeflow is broader around Kubernetes-based orchestration, while Weights & Biases emphasizes experiment management and collaboration; the right choice depends on existing infrastructure and governance needs.
A strong MLflow professional often also works with Python, Docker, Kubernetes, cloud storage, CI/CD and data pipeline tools. Knowledge of Spark, Databricks, model serving and access control is useful when the platform supports production workloads.
The required experience depends on whether the work covers basic tracking, a shared registry or a production platform. An MLflow freelancer should be able to show relevant delivery across experiment lineage, artifact storage, deployment and operational handover rather than only notebook usage.
Yes. MLflow work can be coordinated remotely when repositories, environments and data access are documented clearly. On-site sessions in Munich can still help with architecture decisions, security reviews and alignment with local data or product teams.
Ask how the MLflow specialist would design tracking, artifact storage, registry permissions and promotion rules for your environment. Good answers cover failure handling, reproducibility, observability and a practical migration or rollout plan.
MLflow provides important lifecycle capabilities, but it does not replace every data, compute or orchestration system. Teams may still need cloud infrastructure, workflow scheduling, feature management, monitoring and a suitable deployment environment.
MLflow is closely integrated with Databricks, while remaining usable in independent open-source environments. A freelancer should clarify which features depend on Databricks and how tracking servers, artifact stores, model registries and deployment targets will be managed outside that ecosystem.
The average hourly rate of freelancers in Munich, Germany who have used MLflow in their recent projects is 101 €, which corresponds to a daily rate of about 810 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used MLflow in their recent projects, 100% hold at least a Bachelor's degree, 90% hold at least a Master's degree, and 30% hold a doctorate.
On average, freelancers in Munich, Germany who have used MLflow in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 1.7 years.
The most common languages among freelancers in Munich, Germany who have used MLflow in their recent projects are German (100%), English (92%), and Italian (25%).
The most common industries among freelancers in Munich, Germany who have used MLflow in their recent projects are Information Technology (92%), Professional Services (67%), and Banking and Finance (58%).
The most common business areas among freelancers in Munich, Germany who have used MLflow in their recent projects are Information Technology (100%), Product Development (100%), and Business Intelligence (83%).
Main locations of FRATCH Experts, who have recently used MLflow
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.
Berlin Hamburg 
Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
Berlin Request a free demo
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Philipp Thomaschewski
FRATCH CEO
