AutoML Experts in Germany
matched in minutes from vetted and available specialists with the power of AI.Hire experts who build AutoML workflows, tune model selection and feature engineering, and connect tools like Auto-sklearn, H2O AutoML, and Google Vertex AI. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used AutoML
Haseeb Zahid
Last position:
Senior Data Scientist at WPP MEDIA
- Designed and deployed enterprise Retrieval-Augmented Generation (RAG) applications using LangChain, LangGraph, vector databases, embeddings, and open-source LLMs served through vLLM on GCP GPU infrastructure.
- Built agentic AI workflows using LangGraph with planning, reasoning, tool execution, persistent memory, session management, and Human-in-the-Loop approval mechanisms.
- Developed LLM-powered automation systems integrating BigQuery, SQL pipelines, and external advertising APIs including Meta, TikTok, Amazon, Snapchat, Google, and Pinterest, reducing manual operational workflows.
- Architected multi-agent AI systems for enterprise analytics and decision-support workflows, enabling autonomous task execution and intelligent data interactions.
- Implemented retrieval optimization strategies including multi-retriever architectures, semantic search, context optimization, and query improvement techniques, improving response relevance by approximately 40%.
- Engineered structured prompting strategies, function-calling schemas, and validation workflows to improve reliability of multi-step LLM applications.
- Designed scalable AI services using Python, FastAPI, Cloud Run, Pub/Sub, BigQuery, Docker, and cloud-native deployment architectures.
Julien Look
Last position:
MLOps Engineer at SAMGEN
- Building and scaling cloud infrastructure on GCP to support a SaaS platform for industrial clients
- Designing and implementing a data-driven DevOps pipeline for streamlined deployment and CI/CD workflows
- Collaborating with Data Science team on MLOps workflow to automate integrated retraining
Muntaha Shams
Last position:
AI Engineer (Freelance) at Upwork
- Delivered 40+ AI projects and 23 strategic consultations for international clients (US, Europe, Middle East), achieving a 98% job success rate and building long-term partnerships.
- Developed and deployed production-grade AI solutions in computer vision, NLP, deep learning, and generative AI (LLMs, RAG pipelines, Stable Diffusion, OCR, chatbots), enabling automation and improving client efficiency by up to 70%.
- Designed and fine-tuned large language models (LLMs), including prompt engineering and integration with enterprise knowledge bases, leading to smarter decision-making and reduced manual effort.
- Built real-time computer vision applications (detection, segmentation, OCR) and integrated them into business systems, significantly enhancing accuracy and scalability.
- Consulted startups and enterprises on AI strategy, architecture, and deployment (cloud & on-premise), accelerating product development and reducing time-to-market.
- Managed complete AI project lifecycles (requirements gathering, solution design, deployment, support) in agile, international, and cross-functional environments, ensuring high-quality delivery.
David Thompson-Ajayi
Last position:
AI Trainer (NLP & LLM Evaluation) at Freelance
- Designed and evaluated high-quality prompts and completions for Large Language Models (LLMs), focusing on improving response accuracy, instruction-following behavior, and factual consistency.
- Annotated and rated LLM-generated outputs for grammar, coherence, relevance, and truthfulness.
- Developed RLHF-style preference data by ranking model completions to inform reinforcement learning fine-tuning cycles.
- Participated in prompt engineering experiments to assess the effect of instruction format, verbosity, and phrasing on model behavior.
- Conducted error analysis and quality assurance on large-scale NLP datasets, identifying edge cases and linguistic ambiguity affecting LLM performance.
Daryoosh Dehestani
Last position:
Data Analyst & MLOps-Engineer at CEWE Group
Set up and operated data-driven analysis and reporting processes in Power BI, Tableau, and SAP
Integrated SAP FICO and Workday data into Power Platform workflows to automate HR reports
Developed predictive ML models for workforce planning and KPI management
Used Azure and GCP (BigQuery, Dataflow) to process large data volumes (Big Data pipelines)
Automated reporting increased analysis efficiency by 40%
Introduced a GCP-based analysis model for employee turnover
Pawan Saxena
Last position:
CAPTCHA Recognition using CRNN
- Built a CRNN model with VGG16 and BiLSTM backbone for text-based CAPTCHA recognition
- Achieved 9.37% character error rate and 68.36% sequence accuracy on validation data
- Expanded data augmentation pipeline with distortions, noise injection, and clutter to improve robustness
- Conducted detailed error analysis on confusable characters (O, Q, D) and proposed error-specific augmentation
- Tech Stack: Python, TensorFlow/Keras, OpenCV, NumPy, Matplotlib
Alexis Peters
Last position:
Freelance Team Lead Backend Applications E-Commerce at The Quality Group GmbH
- Building and maintaining an event-driven microservice architecture around Shopify
- Documenting with Confluence and IcePanel (C4)
- Using PHP 8.2, Symfony, Shopify, Bref, AWS, SQS, EventBridge, Cloud, DevOps, Datadog, Docker, Kubernetes, Jira, GitHub, Scrum, Kanban, PHPUnit, Prophecy and Swagger
Anton Klonov
Last position:
Head of Technical Overall Integration NSC / Hadoop Cloud Development at IABG
Head of technical overall integration NSC (National Secure Cloud project with about 60 employees).
Technical integration of all subprojects into one product, definition of interfaces, basic components of a cloud including hardware, technical architecture of the IABG base.
Development of a Cloud Management Platform (CMP) that can create a private/mixed cloud of any complexity based on a textual description with one click or interactively.
CMP also includes the complete hardware management cycle.
As a foundation, it uses Kubernetes, OpenStack, and Hadoop.
The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are automatically configured.
The private cloud can run any customer workloads, including a full Hadoop stack with HDFS, Spark, MapReduce, Mesos, HBase and around 20 other ML/DL technologies.
Hadoop worker clusters can also be automatically installed on bare metal or commodity hardware without Kubernetes.
OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.
Development of a Java application Rudi: SOAP, REST, containers, database.
Technologies: Kubernetes (K3s, RKE2, Minikube, Harbor, Gitea, Jenkins, Longhorn, Keycloak, Rancher), OpenStack (Nova, Neutron, Keystone, Swift, Ceph, Cinder, Sahara, Magnum, Kayobe, Kolla, Bigrost, Ironic), Hadoop (HDFS, Ambari, Solr, Livy, Ranger, YARN, Tez, HBase, Kafka, Hive, Zookeeper, MapReduce, Spark, Oozie, Flink), virtualization (Kubernetes (K3s), VMware, Oracle), scripting (Ansible, Puppet, Juju, Shell, Groovy, Gradle, Maven).
Discover over 15,000 top freelancers
Statistics of experts using AutoML
Aggregated from the professional profiles of matched freelancers.
Experience
11 years
Position duration
1.4 years
Positions per freelancer
11
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Retail, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
88%
Master's degree or higher
75%
Certifications per freelancer
4
Most common languages
English, German, French
Speak two or more languages
88%
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 Germany 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 Germany using AutoML
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 AutoML does
AutoML, short for automated machine learning, helps teams train and compare models with less manual work. It is used for prediction, classification, forecasting, and rapid model baselines when data teams need speed and repeatable results.
Common workflows
- Data preparation and feature checks
- Model selection and benchmarking
- Hyperparameter tuning and validation
- Pipeline setup for repeatable training
- Export for production use or review
Tools and stacks
AutoML work often sits around platforms like H2O AutoML, Auto-sklearn, Google Vertex AI, Azure Machine Learning, and AWS SageMaker Autopilot. Strong specialists know when to use built-in automation and when to move back to custom Python, scikit-learn, or Spark-based work.
When companies bring in help
Teams usually look for freelance expertise when they need a fast proof of concept, a better baseline model, or help comparing vendors and open-source options. In Germany, this is common in teams that want remote support for data projects while keeping workshops or handover sessions on-site.
What strong specialists do
Good AutoML professionals do more than click through a UI. They define the target, set the right evaluation method, watch for leakage and weak data quality, and explain why one model is better than another. They also document the path from experiment to deployment.
What to expect
A solid specialist can work with product, data, and engineering teams, then hand over clean notebooks, pipelines, and model notes. They should be comfortable with Python, SQL, cloud tooling, and the limits of automated machine learning, especially when business rules or explainability matter.
Frequently asked questions
Curious about AutoML? Here are the answers that come up again and again.
AutoML is used to speed up model building for tasks like classification, forecasting, ranking, and anomaly detection. It helps teams compare many model and feature combinations quickly, then choose a strong baseline before deeper tuning starts.
With AutoML, much of the model search, tuning, and pipeline assembly is automated. Manual work gives more control, but it also takes more time and deeper expertise. Many teams use both: AutoML for fast baselines and manual work for the final solution.
A strong AutoML specialist often works with H2O AutoML, Auto-sklearn, Google Vertex AI, Azure Machine Learning, and AWS SageMaker Autopilot. The exact stack depends on whether the project runs in Python, a cloud environment, or a mixed data platform.
Beyond AutoML, good specialists usually know Python, SQL, feature engineering, validation strategy, and basic deployment patterns. They should also understand data quality, model interpretation, and how to hand work over to teams that maintain production systems.
A AutoML freelancer needs a clear target, access to sample data, and a definition of success. The more ambiguous the business problem, the more time is needed to align on labels, metrics, and constraints before model work starts.
Yes, most AutoML work can be done remotely because data review, experimentation, and documentation are digital tasks. For teams in Germany, remote collaboration is common, while on-site time can help with kickoff meetings, stakeholder alignment, or handover sessions.
Look for clear thinking, not just tool familiarity. A strong AutoML specialist explains why a model was chosen, checks for data leakage, can defend the evaluation method, and shows how the result fits the business use case.
Choose AutoML when speed, comparison of many candidate models, or a quick baseline matters more than deep customization. Build from scratch when you need unusual data handling, strict interpretability, or a model design that standard automation cannot support.
The average hourly rate of freelancers in Germany who have used AutoML in their recent projects is 90 €, which corresponds to a daily rate of about 719 € based on an 8-hour working day.
Of the freelancers in Germany who have used AutoML in their recent projects, 88% hold at least a Bachelor's degree and 75% hold at least a Master's degree.
On average, freelancers in Germany who have used AutoML in their recent projects have 11 years of professional experience, with a single engagement typically lasting around 1.4 years.
The most common languages among freelancers in Germany who have used AutoML in their recent projects are English (100%), German (88%), and French (38%).
The most common industries among freelancers in Germany who have used AutoML in their recent projects are Information Technology (100%), Retail (63%), and Banking and Finance (50%).
The most common business areas among freelancers in Germany who have used AutoML in their recent projects are Information Technology (100%), Business Intelligence (75%), and Product Development (75%).
Main locations of FRATCH Experts, who have recently used AutoML
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 Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
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Philipp Thomaschewski
FRATCH CEO
