Keras Experts in Berlin
matched in minutes by AIHire experts who deliver neural network models, computer vision pipelines and TensorFlow-based production workflows with Keras. FRATCH connects you quickly and precisely with vetted, available freelancers suited to your project.
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.
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.
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
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.
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
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: 97%)
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Berlin 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 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 19 Sep 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 designing, training and deploying neural networks. It provides a clear Python interface for TensorFlow workflows and supports common tasks such as image classification, text processing, forecasting and recommendation. Its modular approach helps teams move from experiments to maintainable machine learning systems.
Core capabilities
Keras professionals work across the full model lifecycle:
- Define sequential, functional and subclassed neural network architectures
- Prepare data pipelines, augmentation and feature transformations
- Train models with callbacks, checkpoints and distributed workflows
- Evaluate results, tune hyperparameters and track experiments
- Export models for serving, mobile or embedded inference
They also select suitable loss functions, optimizers and metrics, then document assumptions so results can be reproduced and reviewed.
Ecosystem and tooling
Keras is commonly used with TensorFlow, but current Keras workflows can also target other backends where the project requires it. Relevant tooling includes NumPy, pandas, scikit-learn, TensorBoard, Jupyter and data versioning systems. Strong specialists understand GPU environments, containerized services, model serialization and APIs that expose predictions to business applications.
Where companies use it
Keras appears in products that need learned predictions rather than fixed rules:
- Visual inspection, image search and document classification
- Natural language classification, embeddings and sequence models
- Demand forecasting, anomaly detection and risk analysis
- Personalization and recommendation features
In Berlin, teams across technology, research, manufacturing, mobility and digital services may use it as part of a broader TensorFlow or cloud machine learning stack.
When to bring in a specialist
Freelance expertise is useful when a proof of concept must become a dependable service, when training data needs careful preparation or when a model performs well in a notebook but fails in production. It can also help with GPU utilization, experiment design, monitoring, retraining and integration with existing data systems. Remote collaboration works well when repositories, datasets and acceptance criteria are organized; on-site work may help when the model depends on specialized equipment or close coordination with local teams.
What strong professionals deliver
Look for a specialist who can explain model choices in business terms and show how validation reflects real usage. Quality work includes clean data handling, sensible baselines, reproducible training, meaningful error analysis and a clear deployment path. A strong Keras professional knows when a simpler model is enough, when transfer learning is appropriate and how to balance accuracy, latency, cost and maintainability. They should also communicate clearly in the language and working style your Berlin team needs.
Frequently asked questions
Key details about Keras, drawn from the questions we get asked most.
Keras is used to create, train and deploy neural networks for tasks such as image recognition, text classification, forecasting and recommendations. It helps teams structure machine learning work in readable Python while connecting models to data pipelines and production services.
Keras offers a concise, high-level API that can make common model workflows easier to read and maintain. PyTorch gives specialists more direct control over training behavior and is often preferred for research-heavy or highly customized work, so the choice depends on the team’s stack, model needs and deployment path.
Keras is the standalone deep learning API, while TensorFlow Keras refers to its integration with the TensorFlow ecosystem. The two approaches share familiar concepts, but project requirements and installed versions should be checked before selecting APIs, saving models or planning deployment.
Keras work usually benefits from knowledge of Python, NumPy, pandas, scikit-learn and TensorFlow. For production projects, companies should also look for data pipeline design, experiment tracking, containerization, cloud or GPU operations, model serving and monitoring.
Keras projects do not all require the same depth of expertise. A clear prototype may need a specialist who can prepare data and establish a baseline, while a regulated or high-volume system requires proven skills in validation, reproducibility, deployment, monitoring and retraining.
Keras work is often suitable for remote collaboration because code, experiments and documentation can be shared through standard development tools. On-site coordination may still be valuable when data comes from physical equipment, restricted environments or teams that need frequent German- or English-language workshops.
Keras quality should be assessed through more than a model’s headline accuracy. Ask for a clear validation strategy, baseline comparisons, error analysis, reproducible training steps, documented data assumptions and an explanation of how the model will behave after deployment.
Keras may be unnecessary when a rules-based approach or a conventional statistical model solves the problem more transparently. It can also be a poor fit if the available data is too limited, labels are unreliable or the project has no practical path to maintaining and monitoring a trained model.
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
