Keras Experts in Germany
in minutes from 15,000 CVs with the power of AIHire experts who build and tune Keras models for image, text, and tabular data, connect them to TensorFlow workflows, and turn notebooks into reliable training and inference pipelines. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used Keras
Karin Albiez
Last position:
AI Benchmark Engineer | Native language specialist German at Lilt
- Task Engineering: Evaluating Coding Agents.
- Asset Creation: Building realistic task environments using datasets and files in German. Crucially, these assets must remain in the target language to genuinely measure multilingual handling.
- Prompting & Translation: finding failure points where AI does not work, in German.
- Implementation & Verification: Supporting the development of robust solutions (reference implementations) and write highly reliable, deterministic verifier scripts (using rubric-based judging only when strictly necessary).
- Calibration & Execution: Analyze execution logs and calibrate task difficulty (Easy to Very Hard) using standard Terminal-Bench run configurations against various model tiers (Haiku, Opus).
- Quality Assurance: Participation in a rigorous, 4-layer human quality control process (creation, human review, calibration review, and audit) alongside automated LLM-based checks to ensure fairness, grammatical accuracy, and benchmark integrity.
- Linguistic Review: Reviewing AI benchmark tasks across Hindi, Arabic, Japanese, Chinese, Czech and Turkish.
Michael Nelz
Last position:
Senior ML Engineer, AI Engineer at Lanxess AG
- Deployment and scaling of existing ML initiatives, including demand and cash flow forecasts.
- Building robust monitoring with mlflow for data stability, model performance, and drift detection, as well as implementing additional ML use cases.
- Further development of an Agentic AI chatbot for transparent and easy-to-understand model explanations.
Kiriakos Krastillis
Last position:
Tech Lead / Architect : OTTO API Platform at OTTO
maturing their API Practices on both, a Business and Technology level. My role encompasses strategy, architecture, developer advocacy as well as hands on software engineering, enabling both technical teams and business leadership to adopt and act on API- centric principles effectively. Coincidentally, we also establish GitOps, DX and Platform Best practices with this project.
Highlights:
- Aligning executives with the initiative by clarifying strategy, replacing misconceptions and myths with facts, clarifying the value of existing assets and enabling informed decision-making
- Formulating a way forward for API Lifecycle Management at OTTO
- Driving platform progress and fostering developer engagement by hands-on engineering work towards strategic goals
API Lifecycle Management, Team Topologies, Organizational Evolution, Regulatory, Platform Advocate, Developer Platform, Communities of Practice, Terraform, Kotlin, Kafka, Kong, WSO2, Apigee, Gravitee, Backstage, AsyncAPI, OpenAPI, API Design, AWS, react, nodejs, typescript, redocly, reactive programming, CDC, golang, gingonic, GitOps, DX (developer experience), stakeholder management, roadmaps, workshops, discovery.
Philipp Grunert
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
Danny-Michael Busch
Last position:
Senior AI Engineer at Just Add AI GmbH
- Automatic detection of content on various documents
- Recommendation Engine
- Dynamic Pricing
Mirza Klimenta
Last position:
Agentic AI for a DeepResearch project at Freelance
- Created a multi-agentic system supported by a knowledge graph to automate drafting of research papers
- Used multiple experts (OpenAI models) collaborating during document drafting
- Extracted useful information from the knowledge graph
- Technologies: LangChain, LangGraph, Smolagents, LlamaIndex, dspy
- Infrastructure: Terraform and GitHub Actions (CI/CD) on AWS
- Deployed initial application as a Streamlit app
Shanna Tellaev
Last position:
Problem Resolution Manager at CARIAD SE (VW AG), formerly CARMEQ GmbH (VW AG)
- Automotive SPICE®: all assessments fully achieved
- Agile transformation: V-model → SAFe successfully implemented
- Series release: on-time, quality-assured software delivery for key Volkswagen Group models (including ECE homologation)
- Stakeholder management: internal & external
- Process optimization: implemented a continuous improvement process (CIP) with a tracking system
Afaq Afaq Saeed
Last position:
Master’s Thesis Researcher – Multiview Perception Evaluation at Volkswagen AG
- Developed an evaluation framework for AI-generated multiview driving videos intended for perception and embodied-AI/VLA-related training workflows.
- Designed automated checks for temporal coherence, cross-camera consistency, semantic correctness, and multiview geometric quality, exposing failure modes relevant to autonomous systems.
- Combined classical computer vision, learned visual representations, and vision-language models to convert complex video artifacts into measurable engineering signals.
- Built repeatable benchmarking and failure-analysis workflows to support model comparison, data-quality decisions, and system-improvement discussions.
Rutger Boels
Last position:
Partner & Managing Director at AI.IMPACT
- Building an AI & Data Consultancy Practice with the goal of helping European companies adopt Artificial Intelligence and modern data platforms
- End-to-end further development of a production system using modified coding agents (OpenCode). Tech stack: Kubernetes, Argo, Keycloak, Typescript, Grafana, GitOps, DevOps, Playwright
- Internal research project on the use of coding agents in the field of mathematical logic for creating formal models. Use of Cursor IDE and Codex, Codex CLI. Architecture design, quality control and refactoring, as well as writing code and tests. Repository (open source) available pre-launch
- Research on the role of mathematical logic as a formal language that connects IT and AI with business processes
- Project lead for collecting and deploying parking recommendations for rail vehicles with significant savings potential based on real-time data in a mobility and transport company
- Project lead for collecting and distributing process measurement points for real-time control in a mobility and transport company
- Deputy application owner for an app used for communication in the dispatching and provision of rail vehicles
Cris Lovell-Smith
Last position:
Head of AI at Harvest Hub
- Leading AI development for aquaculture startup, optimising shellfish visual assessments with machine learning and computer vision.
- Development and systematic evaluation of ML/CV algorithms for shellfish condition and morphometrics, using Python, Pytorch and MLFlow.
- Analysis of model performance, including identification of failure modes and edge cases in production deployments.
- Design of annotation strategies and refinement of labelled datasets for computer vision tasks.
- Detailed analysis of system performance and communication of findings through publication-quality technical reports to investors and fellow R&D staff.
- Responsible for delivery of technical roadmap.
Marco Lindner
Last position:
Senior IT Consultant | Cloud Data Engineer | Infrastructure Architect at Hannover Rück SE
Built an enterprise data lakehouse platform on Azure Databricks
Developed production data pipelines and governance structures
Implemented private cloud infrastructures using Terraform
Introduced modern CI/CD standards in Azure DevOps
Implemented secure IAM and governance concepts
Developed scalable PySpark and Delta Lake frameworks
Supported self-service analytics and data product approaches
Provided architecture and platform consulting for enterprise data initiatives
Built a central DataHub architecture for insurance data
Integrated multiple subsystems into a lakehouse platform
Introduced data governance and data lineage
Supported modern analytics and reporting standards
Optimized data delivery for business and analytics teams
Sergei Minkov
Last position:
Program Manager / Program Lead (Contractor) at Telefonica
Program Manager for a radical architecture and IT transformation program (RAITT) reshaping the applications landscape (i.e. cloud transformation) and operating model into agile organisation.
- E2E readiness towards mass-market business division covering demand, delivery, test and roll-out phases
- Driving Telefonica internal teams and external system integrators to ensure delivery on time and in quality in adherence to defined processes
- Management of risks, issues and dependencies on program level
Diogo Soares
Last position:
Backend Engineer and AI Orchestrator at Stealth Startup
- Providing freelance software engineering and AI orchestration services for an early-stage startup.
- Designing and coordinating autonomous AI systems capable of executing complex, multi- step workflows.
- Developing customer-facing pilots and proof-of-concept solutions.
- Participating in meetings with customers and investors to support product development and business discussions.
Raphael Mankopf
Last position:
Founder / Quant Developer at Market Maker
- Crypto quant strategy development, automated trade execution, onchain data client (Ethereum / Solana)
- Data and trade architecture development for liquidity provision
Asad Karim
Last position:
Senior AI Developer at Neuland.ai AG
- Architected and deployed a production-scale GraphRAG system using Neo4j, embeddings, and multi-hop reasoning over 120M+ nodes, improving answer precision by 32%, reducing hallucinations by 41%, and lowering retrieval latency by 38%.
- Designed and implemented an enterprise agent ecosystem using Model Context Protocol (MCP), exposing internal APIs, databases, and services as secure callable tools for autonomous workflows and system integration.
- Designed and deployed a production LLM-based email routing agent using Microsoft Graph API, MCP, and Azure OpenAI, achieving 96% routing accuracy, reducing manual triage workload by 65%, and decreasing response times from 18 hours to under 4 hours.
- Implemented autonomous agent self-correction pipelines using iterative feedback loops (Ralph Wiggum), enabling reliable error detection, automated remediation, and production-safe execution.
- Developed a multimodal semantic search platform using multimodal LLMs and vector embeddings, enabling semantic discovery across 250k+ image and video assets and improving search recall by 48%.
Discover over 15,000 top freelancers
Statistics of experts using Keras
Aggregated from the professional profiles of matched freelancers.
Experience
14 years
Position duration
2.1 years
Positions per freelancer
9
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Education, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
96%
Master's degree or higher
80%
Doctorate
20%
Certifications per freelancer
2
Most common languages
German, English, French
Speak two or more languages
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 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 Keras
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 Keras is
Keras is a high-level deep learning library used to design, train, and deploy neural networks with less boilerplate. It is widely used through TensorFlow, often as tf.keras, for work on computer vision, NLP, forecasting, and other model-heavy systems.
Typical work
- Build and train sequential and functional models
- Add custom layers, losses, and metrics
- Prepare transfer learning workflows
- Set up evaluation, callbacks, and checkpoints
- Export models for serving or edge use
Tools around it
Strong professionals working with Keras usually know TensorFlow, NumPy, Pandas, scikit-learn, and model tracking tools. They also understand GPU setup, data pipelines, and how to move from notebooks to repeatable training code without breaking results.
When to bring in help
Companies bring in freelance expertise when a model is already promising but needs clean implementation, faster training, or better validation. That is common in Germany for product teams, industrial AI work, and internal analytics projects that need remote collaboration in English or close work with local teams.
What strong specialists do
A good Keras specialist writes clear model code, handles data preprocessing, and spots issues in overfitting, leakage, and unstable training. They can compare Keras with PyTorch when needed, but they stay focused on delivering a model that works in production.
Signs you need Keras expertise
- Your prototype works, but training is fragile
- You need custom model behavior or transfer learning
- Your team needs help with tf.keras migration
- Deployment or inference is not yet reliable
- You need better metrics, logs, or reproducibility
Frequently asked questions
Quick answers to the questions that come up most around Keras.
Keras is used to build and train neural networks for tasks like image recognition, text classification, forecasting, and anomaly detection. It is a common choice when a team wants a clear API on top of TensorFlow and needs to move from experiments to reusable model code.
Keras started as a separate high-level library, and many teams now use it through TensorFlow as tf.keras. In practice, people often mean the same workflow, but an experienced specialist should know which API version your codebase uses and how to keep it consistent.
Keras is usually preferred when teams want a concise, structured API and faster model assembly for standard deep learning work. PyTorch is often chosen for more custom research-style code, but Keras remains strong for production-oriented teams that value readability and TensorFlow integration.
A strong Keras specialist should also understand TensorFlow, Python, NumPy, and data preparation with Pandas or scikit-learn. For production work, experience with model evaluation, packaging, and serving is just as important as writing the model itself.
For a simple proof of concept, a general Python and machine learning specialist may be enough. For custom layers, transfer learning, or production training pipelines, you want someone who has shipped serious Keras work and can explain the trade-offs clearly.
Yes, most Keras work can be done remotely if the data access, review process, and deployment path are clear. In Germany, many teams mix remote collaboration with occasional on-site sessions for planning, security reviews, or handover meetings.
Ask for examples of models they have trained, how they handled validation, and what they changed when results were unstable. A strong Keras professional can explain architecture choices, data leakage risks, and why a model improved, not just show a notebook.
A Keras freelancer should clarify the target task, data format, success metric, and where the model will run. It also helps to know whether the work is greenfield, a fix for existing code, or a migration from older TensorFlow or Keras code.
The average hourly rate of freelancers in Germany who have used Keras in their recent projects is 90 €, which corresponds to a daily rate of about 721 € based on an 8-hour working day.
Of the freelancers in Germany who have used Keras in their recent projects, 96% hold at least a Bachelor's degree, 80% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Germany who have used Keras in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Germany who have used Keras in their recent projects are German (98%), English (98%), and French (19%).
The most common industries among freelancers in Germany who have used Keras in their recent projects are Information Technology (84%), Education (54%), and Professional Services (37%).
The most common business areas among freelancers in Germany who have used Keras in their recent projects are Information Technology (89%), Product Development (86%), and Research and Development (76%).
Main locations of FRATCH Experts, who have recently used Keras
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 
Munich 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
