Keras Experts in Berlin
matched in minutes from over 15,000 CVsHire experts who design, train and deploy deep learning models with Keras, TensorFlow and modern multi-backend workflows. Get precise matches with vetted, available freelancers for computer vision, natural language processing and production machine learning projects.
Meet FRATCH Experts in Berlin, who have recently used Keras
Nikolai G.
Last position:
Clinical Data Manager at Dr. Falk Pharma
- Used OpenCode and AI-assisted software engineering to design, implement, refactor, test, and document an end-to-end RAW/SDTM/ADaM pipeline in R for Dr. Falk Pharma (07/2026), including metadata-driven transformations, automated validation rules and QC, traceability, and reproducible clinical outputs.
Raphael M.
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
Mark W.
Last position:
Independent IT/AI Consultant at Freelance
- IT consulting, coaching, and implementation with a focus on AI
Julien L.
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
Tobias J.
Last position:
Design of an AI-Agent-Based ERP System
- Design of an LLM-based agent system to control the ERP software
- Development of agent workflows with LangGraph and PydanticAI
- Planning interfaces between business logic and language models
- Planning agent orchestration
- Prototype development and demonstration
Tools: Python, Pydantic, React, LangChain, LangGraph, Linux
Maurizio F.
Last position:
Python Software Developer at Schönhofer Sales and Engineering GmbH
- Implemented a command line interface (CLI) for integration of REST APIs of various microservices for end users
- Centralized and simplified interaction with services through the CLI
- Implemented a REST microservice for custom data schemas based on an API-first approach
- Developed event-driven control with RabbitMQ to connect to other services
- Deployed services using Docker and Kubernetes and extended the CLI
- Managed complexity and data volume handling through the microservice
Diogo S.
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.
Robin S.
Last position:
Consultant, Data Science & Engineering at valantic Digital Finance GmbH
- Bridged business and engineering for enterprise finance clients, designing data products and cloud pipelines in Python, SQL Server, SAP Datasphere, and Tagetik
- Conceived, built, and containerised a Python/FastAPI universal connector that syncs SAP S/4HANA and other SQL/NoSQL sources to Tagetik, deployed on Google Cloud Run and Microsoft Azure, cutting a critical 90-minute data load to approximately 80 seconds (65Ă— faster)
- Architected a medallion-layer SQL Server warehouse ingesting approximately 500 GB/day from 11 ERP instances, automating daily refreshes (full load under 6 minutes) and freeing 20–30 finance staff from days of manual data consolidation
- Led cross-functional workshops to design enterprise EPM target architecture for a leading Southeast-Asian telecom (CAPEX, OPEX, revenue), translating requirements into data-model specifications and integration blueprints now being built by the client’s implementation team
- Delivered selected projects including a consolidated data & reporting warehouse for a global manufacturer (10 k+ employees), NFI reporting for an international management & technology consultancy, and CAPEX/OPEX planning for a Southeast-Asian telecom (20 k+ employees)
Philipp G.
Last position:
Machine Learning Engineer at docmetric GmbH
- Analyzed patient data for various clients
- Developed complex analysis pipelines
- Performed quality assurance on methods
Kashaf K.
Last position:
AI Consultant / Expert at Siemens Mobility
- Evaluated 45+ AI use cases and developed a prioritization framework for Siemens’ internal AI roadmap.
- Tested internal tools like DRIM, SiemensGPT, Microsoft Copilot; presented evaluation outcomes to stakeholders.
- Identified performance gaps and improved tool adoption by 65%.
- Supported AI knowledge-sharing initiatives, led tool onboarding sessions, and improved team AI literacy.
- Collaborated with engineering, procurement, and digital teams on tool feedback and strategy alignment.
Sanket T.
Last position:
Master of Engineering: Information and Electrical Engineering at Hochschule Wismar
Manasa N.
Last position:
Fundraiser at Talk2Move
- Represented NGOs
- Engaged in persuasive communication and public outreach
- Enhanced collaboration, communication, and decision-making skills
Gönenç O.
Last position:
Freelance Data Analyst at D4C-Ai
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Statistics of experts using Keras
Aggregated from the professional profiles of matched freelancers.
Experience
11 years (Germany: 14 years)
Position duration
2 years (Germany: 2.1 years)
Positions per freelancer
9
Top business areas
Information Technology, Research and Development, Product Development
Top industries
Information Technology, Education, Professional Services
Certification focus areas
Research and Development, Information Technology, Business Intelligence
Bachelor's degree or higher
100% (Germany: 97%)
Master's degree or higher
82% (Germany: 81%)
Doctorate
36% (Germany: 21%)
Certifications per freelancer
2
Most common languages
German, English, French
Speak two or more languages
92% (Germany: 98%)
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 Berlin 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 Berlin 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 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Keras 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 (85%)
- Education (77%)
- Professional Services (54%)
- Healthcare (46%)
- Banking and Finance (31%)
- Government and Administration (31%)
- Energy (23%)
- Manufacturing (23%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Keras does
Keras is a high-level deep learning API for building, training and evaluating neural networks. It provides a clear Python interface for common machine learning workflows while supporting TensorFlow, JAX and PyTorch backends through modern Keras 3. Teams use it to turn research ideas into maintainable models and production services.
Models and use cases
Keras supports image, text, audio and structured-data workloads. Professionals use it for classification, forecasting, recommendation, anomaly detection and generative AI systems.
- Image classification and object detection
- Text classification and sequence modelling
- Time-series forecasting and anomaly detection
- Transfer learning with pretrained networks
Ecosystem and tooling
Strong Keras work often includes TensorFlow, JAX or PyTorch, depending on the deployment target and research needs. Relevant tools include NumPy, pandas, scikit-learn, TensorBoard, KerasTuner and model-serving components such as TensorFlow Serving or ONNX workflows. Specialists also work with GPUs, containers, data pipelines and experiment tracking.
When companies need specialists
Companies bring in freelance Keras expertise when a proof of concept must become a tested product, when model quality has stalled, or when an internal team needs focused support for a new use case. In Berlin, remote collaboration is common, while workshops and handovers may benefit from local availability and clear communication in English or German.
- Establish a reproducible training and evaluation workflow
- Improve data preparation, validation and model performance
- Adapt pretrained models to domain-specific data
- Prepare inference services for cloud or edge deployment
What strong professionals deliver
Experienced Keras professionals connect model design with business and operational requirements. They select suitable architectures, define meaningful validation methods and control data leakage. They also document preprocessing, manage model versions and explain trade-offs so another team can retrain, monitor and maintain the system.
Choosing the right fit
Look for evidence of complete Keras projects rather than isolated notebooks. A strong specialist can discuss dataset quality, baseline models, error analysis, hardware constraints and production monitoring. Ask how they handled reproducibility, explainability, latency and retraining, then match those answers to your industry data, deployment environment and collaboration model.
Frequently asked questions
Key details about Keras, drawn from the questions we get asked most.
Keras is used to create, train and evaluate deep learning models for images, text, audio, time series and structured data. Companies use it for tasks such as classification, forecasting, recommendation, anomaly detection and generative applications.
Keras offers a concise, high-level API that can run with TensorFlow, JAX or PyTorch backends in Keras 3. PyTorch may offer more direct control for research workflows, while TensorFlow provides a broad production ecosystem; the right choice depends on the team, deployment target and existing systems.
A strong Keras specialist usually understands Python, NumPy, pandas and scikit-learn alongside deep learning. Experience with data pipelines, GPU environments, experiment tracking, Docker, cloud services and model serving is also valuable when the model must operate in production.
Keras project needs vary with the risk and scope of the system, not just the model type. A prototype may need focused support, while production work requires expertise in data quality, evaluation, reproducibility, deployment and monitoring from the start.
Keras work is well suited to remote collaboration because code, datasets, experiments and cloud environments can be shared digitally. Teams in Berlin should define access controls, meeting routines, documentation standards and whether occasional on-site workshops or German-language communication are needed.
Keras is a strong choice when a team wants readable model code, fast experimentation and access to established architectures for image tasks. A specialist should still assess dataset size, annotation quality, inference constraints and whether the chosen backend and deployment stack fit the product.
Review whether Keras work includes a clear baseline, separated validation data, reproducible training and meaningful error analysis. Quality also shows in documented preprocessing, versioned models, tests for inference and monitoring plans for data or model drift.
Keras now includes multi-backend workflows through Keras 3, so professionals should understand how code behaves across TensorFlow, JAX and PyTorch. They should also be ready to explain backend-specific limits, migration considerations and how the selected stack affects training, serving and maintenance.
The average hourly rate of freelancers in Berlin, Germany who have used Keras in their recent projects is 92 €, which corresponds to a daily rate of about 736 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Keras in their recent projects, 100% hold at least a Bachelor's degree, 82% hold at least a Master's degree, and 36% hold a doctorate.
On average, freelancers in Berlin, Germany who have used Keras 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 Berlin, Germany who have used Keras in their recent projects are German (100%), English (92%), and French (15%).
The most common industries among freelancers in Berlin, Germany who have used Keras in their recent projects are Information Technology (85%), Education (77%), and Professional Services (54%).
The most common business areas among freelancers in Berlin, Germany who have used Keras in their recent projects are Information Technology (92%), Research and Development (92%), and Product Development (85%).
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 
Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
Munich Request a free demo
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Philipp Thomaschewski
FRATCH CEO
