Keras Experts in Munich
matched in minutes from over 15,000 CVsHire experts who design, train and deploy neural networks with Keras, TensorFlow and Python, from computer vision models to production machine learning services. FRATCH quickly matches you with vetted, available freelancers who fit your technical needs.
Meet FRATCH Experts in Munich, who have recently used Keras
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
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
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...
Stephan B.
Last position:
Freelance Data Scientist at Baier Data & AI Consulting
Narges D.
Last position:
Research Assistant at Hochschule München
Introduced an integrated approach for structural damage detection across concrete, steel, and glass using advanced technologies such as LiDAR and thermal imaging. Highlighted cross-material interactions to enhance diagnostics and enable predictive maintenance.
Developed an NLP-based medical note simplifier that transforms complex clinical instructions into plain, child-level English. Applied prompt engineering with Flan-T5 transformer models to extract patient-relevant actions and rephrase them into clear to-do items. Built dual Flask and Tornado backends with a printable web interface.
Christian S.
Last position:
Data-Scientist/AI Engineer at The Marcom Engine GmbH & Co. KG
- Concept creation and implementing AI Agents in AWS Cloud
- Continuously alignment with stakeholders
- Collaborate with DevOps
- Technologies: Git, CI/CD (GitHub Actions), Python/ML, Streamlit, Deno/typescript, AWS SAM, AWS Bedrock, AWS Lambda, AWS Dynamo DB, AWS S3, AWS Event Bridge etc.
Raghu Ram V.
Last position:
Telco Customer Churn Prediction – End-to-End ML Pipeline at Self-Initiated Project
- Designed and implemented a full machine learning pipeline for churn prediction using the Telco dataset.
- Applied preprocessing techniques including missing value handling, categorical encoding, feature scaling, and PCA.
- Built and compared over 15 models (logistic regression, random forest, XGBoost, etc.) and evaluated them using accuracy, precision, recall, F1 score, ROC AUC, and PR AUC.
- Tuned hyperparameters with GridSearchCV, achieving 80.6% accuracy with random forest and XGBoost.
- Created visual reports (bar plots, heatmaps, radar charts) to interpret model performance and churn drivers.
- Exported reusable pipelines and trained models with joblib for deployment.
Anton K.
Last position:
Head of Overall Technical Integration NSC / Hadoop Cloud Development at IABG
Head of overall technical integration NSC (National Secure Cloud, project with approx. 60 employees).
Technical integration of all subprojects into one product, definition of interfaces and basic components of a cloud including hardware, technical architecture of the IABG platform.
Development of a Cloud Management Platform (CMP) capable of creating private/mixed clouds of any complexity based on a textual description with one click or interactively.
CMP also includes the complete hardware management lifecycle.
Kubernetes, OpenStack and Hadoop are used as the foundation.
The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are configured automatically.
Private cloud can run any customer workloads, including a full Hadoop layer with HDFS, Spark, MapReduce, Mesos, HBase and around 20 additional ML/DL technologies.
Hadoop worker clusters can also be installed automatically without Kubernetes on bare metal or commodity hardware.
OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.
Development of a Java application Rudi: SOAP, REST, containers, DB.
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).
Kaan K.
Last position:
Computer Vision Engineer at Axulus Reply GmbH
- Computer vision engineer responsible for development of industrial vision solutions, beginning as a working student and transitioning to a full-time role in May 2025.
- Designed and implemented vehicle detection and counting models; integrated the pipeline into a cloud-deployed system (Azure) that delivers live analytics dashboards.
- Building an offline print quality assurance system that scans corrugated-board prints on production lines to detect and classify defects such as splashes, impurities and colour deviations, deploying the solution on Jetson edge devices.
- Collaborated with cross-functional teams while focusing on computer vision components, containerization, and deployment.
Vibhu K.
Last position:
Senior Product Manager at MediaMarktSaturn
- Led development and management of advanced data products and reporting solutions, driving €11M revenue in 2023. Hired and mentored a product manager to enhance product capabilities, enabling brands to gain closed-loop measurement insights.
- Defined product vision and strategy for offsite product domain globally, enabling brands to engage their most valuable customers throughout the omnichannel customer journey.
- Initiated and led data-driven martech and adtech innovations, laying groundwork for AI-powered personalization and targeted marketing across 12 countries.
Stephan S.
Last position:
Senior Data/ML Consultant & Technical Lead at Jolin.io
Role: Software Engineer & Applied Mathematician (Mathematical optimization for scheduling; duration: 1 months; team setting: Team of 2, remote; technologies: JuMP, Julia, Pluto, Svelte, JavaScript, TypeScript, JetBrains Space, Terraform, Nomad)
Role: Software & Cloud & Web Engineer (Building scalable data science compute cluster from scratch; duration: 11 months; team setting: Team of 1, on-site; technologies: Terraform, Kubernetes, k8s ingress, k8s services, k8s RBAC, k8s networking, k3s, etcd, S3, DNS, certificates, Julia, Pluto, JavaScript, Tailwind, Astro, npm, Parcel, Preact, MUI, JWT, AWS SQS, AWS RDS, Python, GitLab, GitHub)
Role: AI & Web Engineer (Custom ChatGPT service; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Poetry, LangChain, Tailwind, ChatGPT API, Flask, FastAPI)
Role: Architect & Data Engineer (Central datalake setup and ingestion; duration: 9 months; team setting: Team of 5, remote; technologies: Infrastructure-as-code, AWS CDK, Python, Boto3, PySpark, AWS Glue, IAM, S3, ECS, Fargate, Lambda, Apache Hudi, DeltaLake, Databricks, GitHub, Jira, Miro)
Role: Software Engineer (PoC Julia migration of scikit-decide; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Julia, GitHub)
Eyasu H.
Last position:
Data Scientist at Deutsche Bundesbank
- Developed web scraping scripts to extract and parse over 5000 product data from the Zalando website.
- Performed ETL processes using Apache Spark in CDSW, loaded the data into the Hadoop ecosystem (HDFS), and managed data using Hive and Impala.
- Implemented machine learning algorithms, achieving 85–90% accuracy on multi-class product classification.
- Integrated Zalando's product and price data into the dashboard with Otto and Takko for interactive visuals.
Daniel C.
Last position:
Founder & Managing Director at BotCraft GmbH
- Building the company with a focus on connectivity for IIoT and Industry 4.0, iRPA/process automation, advanced robotics and smart systems, sensors and services
- Project management and software architecture for IoT gateway development (since 2020) with protocol translation, IT/OT convergence and GRC
- Developing RPA bots for automating and monitoring industrial processes with an agent-based AI approach (since 2020)
- Implementing unsupervised clustering and anomaly detection for time series data in big data streaming pipelines (since 2021)
- Introducing a Docker-based release train for OTA updates with DevSecOps and CI/CD (since 2018)
Marius H.
Last position:
Senior Project Manager / SAFe Solution & Release Train Engineer (Lead Scrum Master) at Telefonica O2
- Led the largest agile project at Telefonica O2 (€21+ million, 11 Scrum & system teams in Germany and India, 80+ developers, 3 vendors) using SAFe, Scrum and Kanban. A showcase project at Telefonica for agile scaling.
- Scaled agile processes to a large project using SAFe.
- Delivered the project at half the cost and time compared to similar projects.
- Test management / coordination of system testing.
- Coached teams (SAFe & Scrum).
- Rollout planning/management.
Discover over 15,000 top freelancers
Statistics of experts using Keras
Aggregated from the professional profiles of matched freelancers.
Experience
17 years (Germany: 14 years)
Position duration
2 years (Germany: 2.1 years)
Positions per freelancer
13 (Germany: 9)
Top business areas
Product Development, Information Technology, Business Intelligence
Top industries
Information Technology, Automotive, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100% (Germany: 97%)
Master's degree or higher
88% (Germany: 81%)
Doctorate
18% (Germany: 21%)
Certifications per freelancer
3 (Germany: 2)
Most common languages
German, English, Spanish
Speak two or more languages
100% (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 Munich 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 Munich 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 (94%)
- Automotive (65%)
- Banking and Finance (65%)
- Manufacturing (53%)
- Professional Services (47%)
- Education (41%)
- Healthcare (35%)
- Insurance (35%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Keras fundamentals
Keras is a high-level deep learning API for building, training and evaluating neural networks. It is commonly used with TensorFlow and Python, while also supporting other backends through its multi-backend design. Its clear model-building approach helps teams move from experiments to maintainable machine learning systems.
Models and applications
Keras specialists create models for image, text, audio and time-series data. Typical deliverables include:
- Image classification and object detection pipelines
- Text classification, embeddings and sequence models
- Forecasting and anomaly detection services
- Recommendation and generative AI prototypes
The right design depends on data quality, latency, explainability and how the model will be used in production.
Ecosystem and tooling
Strong Keras work usually includes TensorFlow, NumPy, pandas and scikit-learn, with notebooks or scripts for repeatable experiments. Professionals may use convolutional, recurrent and transformer architectures, transfer learning, data augmentation and custom training loops. They also understand GPU workloads, model serialization and experiment tracking.
When expertise matters
Companies bring in freelance Keras expertise when a proof of concept needs reliable validation, an existing model needs better accuracy or a data science project must reach production. Useful signals include:
- Training runs that are difficult to reproduce
- Slow inference or excessive resource use
- A gap between research code and a deployable service
- A need to adapt a pretrained model to domain data
A specialist can clarify the architecture, data pipeline and delivery plan before implementation expands.
Production delivery
Keras models can be exposed through APIs, batch jobs, edge applications or cloud services. Delivery may involve TensorFlow Serving, TensorFlow Lite, Docker, Kubernetes and monitoring for drift, latency and failed predictions. In Munich, remote collaboration is common, while on-site work can help when model development depends on local product, manufacturing or research teams.
Signs of quality
A strong professional explains why an architecture fits the data instead of selecting layers by habit. They separate training, validation and test data, document preprocessing, track experiments and test failure cases. They also consider model security, bias, reproducibility and the operational cost of inference. Clear communication in English or German can support collaboration with Munich-based teams.
Frequently asked questions
Curious about Keras? Here are the answers that come up again and again.
Keras is used to build and train neural networks for computer vision, natural language processing, forecasting, recommendation and audio analysis. It can support an early prototype, a model evaluation workflow or a production service when paired with suitable deployment tools.
Keras emphasizes a concise, high-level API and integrates closely with TensorFlow, while PyTorch is often chosen for its flexible imperative workflow and research tooling. The better choice depends on the team’s existing stack, deployment target, debugging preferences and model requirements.
Keras work benefits from strong Python, statistics and data preparation skills, plus knowledge of TensorFlow, NumPy and scikit-learn. For delivery, look for experience with APIs, containers, cloud infrastructure, GPU workloads, model monitoring and data versioning.
Keras project needs vary with the scope, data quality and risk of the use case. A specialist who has delivered a similar model and can explain validation, deployment and maintenance choices is usually more valuable than someone selected by a broad technology list alone.
Keras projects are often well suited to remote collaboration because code, experiments and model artifacts can be shared through standard development tools. On-site sessions in Munich may still help with data access, product workshops, manufacturing workflows or close coordination with research teams.
Keras can be used in production when the surrounding pipeline handles data validation, model versioning, testing, deployment and monitoring. A specialist should show how the model will meet latency, reliability, security and retraining requirements rather than treating training as the complete solution.
Keras quality is visible in reproducible experiments, sound data splits, meaningful evaluation metrics and clear error analysis. Ask the professional to explain preprocessing, baseline comparisons, edge cases and how the model behaves after deployment.
Keras is the standalone deep learning API, while tf.keras refers to its integration within TensorFlow. A specialist should know the version and backend involved, understand compatibility considerations and choose the API path that fits the project’s training and deployment environment.
The average hourly rate of freelancers in Munich, Germany who have used Keras in their recent projects is 100 €, which corresponds to a daily rate of about 803 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Keras in their recent projects, 100% hold at least a Bachelor's degree, 88% hold at least a Master's degree, and 18% hold a doctorate.
On average, freelancers in Munich, Germany who have used Keras in their recent projects have 17 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers in Munich, Germany who have used Keras in their recent projects are German (100%), English (100%), and Spanish (29%).
The most common industries among freelancers in Munich, Germany who have used Keras in their recent projects are Information Technology (94%), Automotive (65%), and Banking and Finance (65%).
The most common business areas among freelancers in Munich, Germany who have used Keras in their recent projects are Product Development (94%), Information Technology (82%), and Business Intelligence (71%).
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 
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FRATCH CEO
