MLOps Experts in Frankfurt
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Meet FRATCH Experts in Frankfurt, who have recently used MLOps
Eduard Van Kleef
Last position:
Workshop Leader 'Introduction to AI Development Tools' at Software company in Wiesbaden
- Presentation introducing generic AI and large language models
- Explanation of legal frameworks (EU AI Act, US CLOUD Act, GDPR)
- Systematic review of AI tools along the SDLC and holistic systems
- Comparison of on-prem LLMs vs. cloud-based, as well as change management and works council
- Facilitated the discussion and derived next steps for introducing AI development tools
Mathew Divine
Last position:
Data Science Expert and AI Strategist at Freelancer
- Built an API to ingest, clean, translate, and index EU tenders documents in Neo4j, enabling hybrid search with RAG and Cypher queries via a Streamlit dashboard
- Deployed the API on AWS Lightsail container services with CI/CD automation via GitHub Actions, ensuring stability through pytest unit and integration tests
- Designed and developed a comprehensive online course on data analysis using ChatGPT for professionals and learners, creating instructional videos and interactive Jupyter notebooks
- Utilized OBS and professional audio equipment to ensure high-quality video and audio content
- Led a CRM data normalization and cleaning project visualized via a Sankey diagram to aid customer understanding and pipeline development
- Implemented and validated a genAI-driven web crawling strategy on AWS, ensuring data quality, scalability, and CRM data augmentation
Leonard Hußke
Last position:
Freelance Software Engineer & Cloud Architect at Leonard Hußke - IT Solutions
- Evaluation of potential providers (Snowflake vs Databricks) and design of the analytics data platform using Databricks
- Data storage and ingestion layer with Amazon S3
- Creation of ETL processes and data transformations with AWS Glue and Databricks Notebooks
- Orchestration with AWS Glue Workflow, Databricks Workflow and Databricks DLT
- Processing of unstructured data including text, image and video
- Databricks workspace setup and administration
- Setting up a medallion architecture to ensure data quality
- Evaluation of possible BI tools (Power BI, AWS QuickSight, Tableau)
- Establishing MLOps using MLflow
- Introducing data governance and data lineage using Unity Catalog
Michael Yaco
Last position:
Senior Consultant, Senior DevOps Engineer at DB Regio AG
- Supported implementation and operation of a portal used online and offline in customer-facing vehicles
- Automated processes by introducing CI/CD pipelines
- Provided enablement and methodological guidance for adopting software engineering best practices
- System environment: NestJS, Node.js, npm, AWS, Docker, Docker Swarm, GitLab CI, WhiteSource, PostgreSQL, Prometheus, Grafana, OpenSearch, REST API
Sibi Lakshmanan
Last position:
Founder (Product Initiative) at Regu-AI
- Founded Regu-AI, an enterprise-grade AI governance and compliance platform integrating EU AI Act, ESG, and CSRD frameworks to help organizations operationalize responsible AI.
- Developed proprietary modules for AI Maturity Assessment, Measurability, and Battery Passport compliance.
- Built and scaled the product architecture and designed a 45-KPI AI governance index, positioning Regu-AI as a first mover in AI governance automation across Germany and the EU.
Anton Rösler
Last position:
AI-Engineer at Publicly traded company, industrial safety technology
- Designed and implemented the agent-based AI architecture for a company-wide platform to securely deploy LLM-based agents
- Designed and implemented end-to-end RAG pipelines from multiple sources: document preprocessing, chunking strategies for different document types, embeddings, retrieval with re-ranking, and robust prompt orchestration
- Developed a modular context engineering framework with skill architecture, context isolation, and dynamic resource management; human-in-the-loop control for enterprise tool integrations
- Built the CI/CD pipeline, testing strategy, tracing on the software side as well as automated LLM and agent evaluations, red team testing and tracing, and handed over to a reproducible production environment (ISO27001 and SOC2 compliant)
Discover over 15,000 top freelancers
Statistics of experts using MLOps
Aggregated from the professional profiles of matched freelancers.
Experience
11 years (Germany: 13 years)
Position duration
2 years (Germany: 2.9 years)
Positions per freelancer
8 (Germany: 9)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Manufacturing, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
100% (Germany: 99%)
Master's degree or higher
60% (Germany: 78%)
Doctorate
20% (Germany: 23%)
Certifications per freelancer
4 (Germany: 3)
Most common languages
German, English, Arabic
Speak two or more languages
100% (Germany: 97%)
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Frankfurt 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 Frankfurt using MLOps
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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What MLOps covers
MLOps, short for machine learning operations, brings software practices to model work. It connects data preparation, training, testing, deployment, and monitoring so models can run reliably after launch. Strong experts know how to turn one-off notebooks into repeatable systems.
Where it is used
Companies bring in MLOps for production ML in fraud detection, forecasting, recommendations, search, and risk scoring. It fits teams that need models to refresh often, stay traceable, and work with existing data and software stacks.
Core toolchain
- Pipeline orchestration for training and retraining
- Experiment tracking and model registry
- Container-based deployment and release automation
- Monitoring for drift, data quality, and model health
- Governance, versioning, and approval flows
Kubeflow, MLflow, Airflow, Docker, Kubernetes, and cloud ML services often appear in the same setup. The right specialist chooses tools that fit the team, the model type, and the production environment.
When to bring in help
Many companies need outside experts when model work leaves the lab and must support users, audits, or internal teams. That is common in Frankfurt’s finance, logistics, insurance, and enterprise software environments, where reliability and clear release control matter.
What strong specialists do
Strong MLOps professionals think across data, code, infrastructure, and operations. They write clear pipelines, set useful checks, and make failures visible early. They also document how a model was trained, shipped, and updated so the system can be maintained.
What good delivery looks like
- Reproducible training and deployment steps
- Clear rollback and approval paths
- Monitoring that shows drift and performance issues
- Shared ownership between data and software teams
- Documentation that supports support, audit, and handover
Frequently asked questions
Before you brief your next project: the most common questions about MLOps.
MLOps is used to move machine learning models into production and keep them healthy after release. It covers training pipelines, deployment, monitoring, and retraining so models do more than sit in a notebook. Companies use it when model behavior must stay reliable over time.
MLOps sits between both, but it is not the same as either one. Data engineering focuses on data flows, while DevOps focuses on software delivery and runtime stability. MLOps adds model-specific work such as experiment tracking, model versioning, drift monitoring, and retraining.
A strong MLOps specialist often works with MLflow, Kubeflow, Airflow, Docker, Kubernetes, and cloud services from AWS, Azure, or Google Cloud. The exact stack depends on how the team trains models and where they run in production. Good experts care less about trendy tools and more about fit and maintainability.
A good MLOps professional usually understands Python, Linux, containers, CI/CD, cloud infrastructure, and basic data workflows. They also need enough knowledge of statistics and model behavior to spot issues like drift or bad evaluation. Communication matters too, because the work touches data, software, and operations teams.
MLOps help is useful as soon as a model must be released more than once or shared beyond one person. If a project only needs a one-off prototype, the setup can stay simple. If it must be repeatable, auditable, or monitored in production, specialist input saves time and risk.
Most MLOps work can be done remotely because the main tasks are in code, pipelines, and cloud systems. On-site time in Frankfurt can help when teams need fast alignment with internal security, infrastructure, or business owners. Many projects use a mixed setup with remote delivery and a few local workshops.
Look for someone who can explain the full path from data to deployment in clear steps. A strong MLOps expert shows working pipelines, monitoring, rollback logic, and documentation, not just a training script. They should also ask about ownership, release flow, and who responds when a model behaves badly.
No, MLOps is useful for small teams as soon as model work needs structure. Even a compact setup benefits from versioning, testing, deployment automation, and monitoring. The scope can stay light, but the process should still be repeatable and easy to maintain.
The average hourly rate of freelancers in Frankfurt, Germany who have used MLOps in their recent projects is 115 €, which corresponds to a daily rate of about 921 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used MLOps in their recent projects, 100% hold at least a Bachelor's degree, 60% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used MLOps in their recent projects have 11 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers in Frankfurt, Germany who have used MLOps in their recent projects are German (100%), English (100%), and Arabic (17%).
The most common industries among freelancers in Frankfurt, Germany who have used MLOps in their recent projects are Information Technology (83%), Manufacturing (50%), and Professional Services (50%).
The most common business areas among freelancers in Frankfurt, Germany who have used MLOps in their recent projects are Information Technology (100%), Product Development (100%), and Business Intelligence (83%).
Main locations of FRATCH Experts, who have recently used MLOps
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 Request a free demo
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Philipp Thomaschewski
FRATCH CEO
