Keras Experts in Germany
matched in minutes by AIHire experts who design neural networks, train TensorFlow models and productionize computer vision or NLP solutions with Keras. FRATCH connects you quickly with vetted, available freelancers whose skills match your project needs precisely.
Meet FRATCH Experts in Germany, who have recently used Keras
Kiriakos K.
Last position:
Tech Lead / Architect : OTTO API Platform at OTTO
Maturing their API practices on both a business and technology level. My role covers 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, Node.js, TypeScript, Redocly, reactive programming, CDC, Golang, Gin, GitOps, DX (developer experience), stakeholder management, roadmaps, workshops, discovery.
Michael N.
Last position:
Senior AI Engineer | Forward Deployed Engineer at Tiefbau
- Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
- Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
- Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.
Mirza K.
Last position:
Agentic Automation and a RAG system
- This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.
Used: Python, RAG, LangGraph, LangChain, deepeval, MCP
Karin A.
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.
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.
Philipp G.
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 B.
Last position:
Senior AI Engineer at Just Add AI GmbH
- Automatic detection of content on various documents
- Recommendation Engine
- Dynamic Pricing
Shanna T.
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 A.
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 B.
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 L.
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.
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
Tobias N.
Last position:
Enterprise & Solutions Architect
- Building an independent enterprise IT setup — cloud strategy, network, AWS landing zone, security requirements, contract negotiations.
- Migration of all applications; avoiding high contractual penalties for the client.
- Onboarding and coordination o...
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
97%
Master's degree or higher
81%
Doctorate
21%
Certifications per freelancer
2
Most common languages
German, English, French
Speak two or more languages
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 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 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 (54%)
- Professional Services (39%)
- Automotive (35%)
- Banking and Finance (35%)
- Healthcare (35%)
- Manufacturing (32%)
- Retail (23%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Keras is
Keras is an open-source deep learning API for building, training and evaluating neural networks. Its clear Python interface runs most commonly with TensorFlow, while also supporting JAX and PyTorch backends. Keras helps teams move from experiments to maintainable machine learning services.
What teams build
Keras is used for image classification, object detection, segmentation, forecasting, recommendation and natural language processing. Experts create models for manufacturing inspection, healthcare research, financial risk analysis, logistics and other data-driven systems. They also adapt pretrained networks to domain-specific datasets.
- Computer vision pipelines for images and video
- Text classification, embeddings and sequence models
- Time-series forecasting and anomaly detection
- Model training, evaluation and inference services
Ecosystem and tooling
Strong Keras work includes TensorFlow, TensorFlow Data, TensorFlow Serving and TensorFlow Lite, as well as NumPy, pandas and scikit-learn. Professionals may use JAX or PyTorch through newer Keras backends and track experiments with tools such as MLflow or Weights & Biases. Familiarity with GPUs, containers, APIs and cloud infrastructure supports reliable delivery.
When companies need experts
Companies often bring in freelance Keras specialists when an internal team has data but lacks deep learning capacity, or when a promising prototype must become a tested service. In Germany, remote collaboration is common, while regulated or hardware-focused projects may require on-site workshops. Clear documentation helps distributed teams work across languages and locations.
- Selecting an architecture and training strategy
- Preparing datasets and augmentation workflows
- Fine-tuning pretrained models
- Reducing latency, memory use or serving costs
What delivery involves
A Keras project usually starts with data validation, a measurable objective and a baseline model. The specialist then designs the model, builds reproducible training code, evaluates errors and packages inference for the target environment. Deliverables can include notebooks, tested Python modules, saved model artifacts, serving endpoints and operational documentation.
How to assess specialists
Look for professionals who can explain why a model works, where it fails and how the evaluation reflects the business problem. Practical evidence includes versioned data and code, repeatable training, meaningful validation splits and monitoring after release. Strong specialists also understand class imbalance, leakage, overfitting, explainability and the limits of model confidence.
Frequently asked questions
Quick answers to the questions that come up most around Keras.
Keras is used to build and train deep learning models for computer vision, natural language processing, forecasting, recommendation and anomaly detection. It is often chosen when a team wants readable Python code and a structured path from experimentation to deployment.
Keras provides a high-level API that can run with TensorFlow, JAX or PyTorch backends. PyTorch may offer more direct control for highly customized research workflows, while TensorFlow provides a broad production ecosystem; the right choice depends on team skills, deployment targets and model requirements.
A strong Keras specialist usually works comfortably with Python, NumPy, pandas, scikit-learn and SQL. Experience with data pipelines, GPU environments, Docker, cloud services, experiment tracking and model serving is valuable when the work extends beyond a notebook.
For a small proof of concept, a specialist who understands data preparation, model selection and evaluation may be sufficient. Production work with Keras calls for evidence of reproducible training, testing, deployment, monitoring and troubleshooting on data similar to the project.
Yes, most Keras work can be completed remotely when data access, environments and objectives are well defined. On-site sessions can still help with workshops, secure infrastructure, hardware integration or close collaboration with teams in Germany.
With Keras, transfer learning is useful when a suitable pretrained model can provide meaningful representations for the target task. It can reduce training effort, but the specialist must still check licensing, domain differences, label quality and whether fine-tuning introduces bias or overfitting.
Ask for a clear link between the business objective, data split, metrics and error analysis. High-quality Keras work includes reproducible code, documented assumptions, baseline comparisons and tests that reflect real inference conditions rather than relying on a single attractive score.
A Keras project may involve more than model design: data rights, labeling, infrastructure, latency, monitoring and handover often determine its success. Freelancers should clarify the backend, dataset access, deployment environment, acceptance criteria and who will maintain the model after delivery.
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 718 € based on an 8-hour working day.
Of the freelancers in Germany who have used Keras in their recent projects, 97% hold at least a Bachelor's degree, 81% hold at least a Master's degree, and 21% 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 (18%).
The most common industries among freelancers in Germany who have used Keras in their recent projects are Information Technology (85%), Education (54%), and Professional Services (39%).
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 (77%).
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
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Philipp Thomaschewski
FRATCH CEO
