MLOps Experts in Frankfurt
to productionise machine learning with fast, precise AI matchingHire experts who build reliable machine learning delivery pipelines, automate model deployment and monitoring, and connect data science with cloud operations. FRATCH matches you quickly with vetted, available freelancers suited to your technical needs.
Meet FRATCH Experts in Frankfurt, who have recently used MLOps
Bardiya B.
Last position:
Data Scientist at Rewe Digital GmbH
Statistical Forecasting Algorithm
- Improvement of an statistical probabilistic forecasting algorithm for sales + evaluation
- Migration from R/On-premise to Python/Snowflake
- Productionalization on Snowflake in cooperation with data engineers & DevOps
Monitoring Dashboard
- Data engineering for preparation & provisioning of necessary data/resources on Snowflake
- Development & deployment of a Streamlit dashboard in Snowflake
ML-based Probabilistic Forecasting on Vertex AI
- Development of a ML-based probabilistic forecasting algorithm from scratch
- Implementation of MLOps pipeline in Kubeflow on Google Cloud Vertex AI
Tech Stack: Python, Snowflake/Snowpark, R, Streamlit, Gitlab/Gitlab CICD, Terraform, Google Cloud, Vertex AI (aiplatform SDK, gcloud CLI, feature store, model registry, etc), kubeflow
Eduard V.
Last position:
Lead of workshop “Introduction to AI Development Tools” at Software company in Wiesbaden
- Presentation introducing generic A.I. and Large Language Models
- Explanation of legal frameworks (EU AI Act, US CLOUD Act, DSGVO)
- Systematic review of AI tools along the SDLC and holistic systems
- Comparison of on-prem LLMs vs. cloud-based solutions, as well as change management and works council considerations
- Moderation of the discussion and definition of next steps for introducing AI development tools
Mathew D.
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
Anton R.
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)
Michael Y.
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 L.
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.
Leonard H.
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, images and video
- Databricks workspace setup and administration
- Implementation of a medallion architecture to ensure data quality
- Evaluation of potential BI tools (Power BI, AWS Quicksight, Tableau)
- Establishment of MLOps using MLFlow
- Introduction of data governance and data lineage using Unity Catalog
Discover over 15,000 top freelancers
Statistics of experts using MLOps
Aggregated from the professional profiles of matched freelancers.
Experience
10 years (Germany: 13 years)
Position duration
1.9 years (Germany: 2.9 years)
Positions per freelancer
8 (Germany: 9)
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, Research and Development
Bachelor's degree or higher
100% (Germany: 99%)
Master's degree or higher
67% (Germany: 77%)
Doctorate
33% (Germany: 23%)
Certifications per freelancer
6 (Germany: 3)
Most common languages
German, English, French
Speak two or more languages
100% (Germany: 97%)
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 Frankfurt 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 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 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
MLOps 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 (86%)
- Professional Services (57%)
- Banking and Finance (43%)
- Manufacturing (43%)
- Advertising (29%)
- Education (29%)
- Energy (29%)
- Transportation (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What MLOps covers
MLOps, short for machine learning operations, applies software delivery and infrastructure practices to the full machine learning lifecycle. It connects data preparation, experiment tracking, model training, deployment, monitoring and retraining so models can run reliably in production. The discipline covers both technical systems and the processes that keep models reproducible, explainable and maintainable.
Production use cases
MLOps supports machine learning products in manufacturing, finance, healthcare, retail, logistics and media. Common deliverables include:
- Reproducible training and validation pipelines
- Model registries with approval and promotion workflows
- Real-time inference services and batch prediction jobs
- Monitoring for drift, data quality, latency and model performance
- Automated retraining and rollback processes
Ecosystem and tooling
Strong MLOps work can span Python, SQL, Docker, Kubernetes and infrastructure as code. Specialists may use MLflow, Kubeflow, Airflow, DVC, GitHub Actions, GitLab CI/CD or cloud services such as AWS SageMaker, Google Vertex AI and Azure Machine Learning. The right stack depends on data volume, compliance needs, deployment targets and the team’s existing platform.
When companies bring in experts
Companies often seek freelance MLOps expertise when prototypes must become dependable products, data science workflows are difficult to reproduce or deployments rely on manual steps. It is also useful during cloud migrations, platform standardisation, model governance work or a rapid expansion of machine learning use cases. In Frankfurt, collaboration may combine remote delivery with on-site workshops for teams in finance, industry and logistics.
Skills that matter
A capable professional understands more than model training. They can design CI/CD for machine learning, manage feature and data pipelines, secure cloud resources, package services with containers and establish observability. They also know how to document lineage, handle secrets, control access and work with data science, software and operations teams. Clear communication in English, and where needed German, helps when stakeholders are spread across Frankfurt and beyond.
Choosing the right specialist
Look for evidence of production systems rather than isolated notebooks. Ask how the specialist handled model versioning, reproducibility, drift, rollback, cost control and failures in real environments. A strong professional explains trade-offs between managed cloud services and self-hosted tools, defines measurable acceptance criteria and leaves behind tested pipelines, useful documentation and operational ownership for the internal team.
Frequently asked questions
Before you brief your next project: the most common questions about MLOps.
MLOps is used to move machine learning models from experiments into dependable production services. It covers training automation, deployment, monitoring, version control, data validation and retraining so teams can operate models throughout their lifecycle.
MLOps combines DevOps practices with concerns that are specific to machine learning, such as model lineage, changing data, experiment tracking and performance drift. DataOps focuses mainly on reliable data workflows, while MLOps connects those workflows with model delivery and operation.
A strong MLOps professional often brings cloud infrastructure, Kubernetes, Docker, CI/CD, Python, SQL and infrastructure-as-code skills. Experience with MLflow, Kubeflow, Airflow, feature stores, observability and security is also valuable, depending on the project.
The right level of MLOps experience depends on the system’s risk, scale and maturity. A focused pipeline improvement may need a specialist who can work within an existing stack, while a new production platform calls for someone who can design architecture, governance and operating processes.
MLOps work is often suitable for remote collaboration because repositories, cloud environments and monitoring tools are accessed online. Frankfurt teams may still prefer on-site workshops for architecture decisions, access reviews or handover, with English or German used according to the stakeholders involved.
Ask for concrete examples of MLOps systems that reached production and examine the decisions behind them. Discuss model rollback, data quality checks, secrets management, monitoring, incident response and documentation rather than focusing only on tool names.
Common MLOps tools include MLflow, Kubeflow, Airflow, DVC, Docker, Kubernetes and CI/CD systems such as GitHub Actions or GitLab CI/CD. Cloud environments may add AWS SageMaker, Google Vertex AI or Azure Machine Learning, but the tooling should fit the delivery and governance requirements.
High-quality MLOps work makes training and deployment reproducible, exposes useful operational signals and supports safe model changes. Teams should review pipeline tests, lineage, monitoring coverage, recovery procedures, access controls and whether another professional can operate the system from its documentation.
The average hourly rate of freelancers in Frankfurt, Germany who have used MLOps in their recent projects is 118 €, which corresponds to a daily rate of about 945 € 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, 67% hold at least a Master's degree, and 33% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used MLOps in their recent projects have 10 years of professional experience, with a single engagement typically lasting around 1.9 years.
The most common languages among freelancers in Frankfurt, Germany who have used MLOps in their recent projects are German (100%), English (100%), and French (29%).
The most common industries among freelancers in Frankfurt, Germany who have used MLOps in their recent projects are Information Technology (86%), Professional Services (57%), and Banking and Finance (43%).
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 (86%).
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
