Streamlit Experts in Munich
matched in minutes from over 15,000 CVsHire experts who turn Python models, data workflows and interactive dashboards into clear Streamlit applications, with precise matching to vetted and available freelancers in Munich.
Meet FRATCH Experts in Munich, who have recently used Streamlit
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
Heidi A.
Last position:
Venture Developer in Product Design at TUM Venture Labs
- Taught German language and mathematics to children aged 4-16, providing homework assistance and tutoring
- Supported startups in UX, MVP development, and Lean Startup methodology
- Assisted with branding, communication, and design to strengthen market presence
- Maintained website and Venture Lab app; coordinated and ran events
- Developed presentations to support internal and external communications
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.
Clarissa H.
Last position:
AI Trainer at Komdis GmbH
- Led comprehensive AI workshops for professionals, focusing on AI-driven process automation.
- Tech Stack: n8n, Make, LLMs (OpenAI, Anthropic), Prompt Engineering, Process Mapping Tools.
Luis Alberto P.
Last position:
Cloud Engineer at Personal Projects
Developed a Streamlit ML application utilizing a RandomForest model (Scikit-learn) for predicting smoking behavior, employing Pandas, NumPy, and Matplotlib for data analysis and visualization; deployed on AWS using Terraform for EC2, IAM roles, and S3 buckets, with Pickle for model storage.
Mastered AWS services including S3, EC2, CloudFormation, IAM, and Auto Scaling, focusing on advanced features like versioning, CORS, ETags, and checksums through AWS-Examples-Freecodecamp.
Developed and optimized CI/CD pipelines with GitHub Actions to deploy static websites on GitHub Pages, enhancing automated validation, deployment, and maintenance processes.
Created and deployed a classic Snake game using Flask, containerized with Docker and deployed on Render.
Azada H.
Last position:
AI Consultant at Freelance
- Built scalable end-to-end machine learning pipelines for a major telco company, covering feature engineering, model development, deployment, and a Streamlit visualization app.
- Initiated and embedded data science within the Customer Experience team, collaborating daily with stakeholders to deliver end-to-end solutions; under my ongoing support, customer satisfaction score, NPS, remained stable at a record >30pt.
- Advised a client on GenAI tools, AI development strategies, and Responsible AI practices, shaping internal adoption and governance approaches.
Max R.
Last position:
Cloud (AWS) | AI | DevOps | Data at Boehringer Ingelheim
- Architected and implemented an enterprise-grade AI Agent Platform leveraging Retrieval Augmented Generation (RAG) architecture to enhance clinical data insights.
- Established robust CI/CD pipelines for LLM applications using CDK and Jenkins, significantly reducing deployment times.
- Implemented comprehensive observability solutions that increased agent reliability across pharmaceutical environments.
- Designed scalable AI workflows with advanced orchestration that optimized context handling for enterprise data sources.
- Technologies: AI Agents (LangChain, LangGraph, Bedrock, Smolagents, Streamlit); LLM Operations (Tracing, Testing, Evaluation, LangSmith, LangFuse); Infrastructure-As-Code (AWS CDK, Terraform, Typescript, Jenkins); Vectors, Embeddings, RAG (OpenSearch, pgvector, PDF Extraction)
Mario G.
Last position:
Software and Data Engineer at Plexify GmbH
- Architecture, design and development of an MVP application for a provider of specialized travel experiences
- Technologies: Python, FastAPI, Firestore, Firebase, Docker
Adithya B.
Last position:
Edge AI Software Engineer at Neura Robotics GmbH
- Deployed and optimized Vision-Language-Action (VLA) and diffusion policy models on NVIDIA Jetson Orin and Jetson Thor, meeting real-time inference latency targets for humanoid robot control loops.
- Built TensorRT engine pipelines (PyTorch → ONNX → TensorRT) with INT8/FP8 post-training quantization, calibration dataset design, and quantization-aware validation, reducing inference memory footprint by over 3× on Jetson without accuracy regression.
- Developed custom CUDA C++ plugins and CUDA Graphs for latency-deterministic, real-time policy execution – meeting hard runtime and memory constraints on embedded GPU targets.
- Developed an inference engine for VLA models on top of llama.cpp bringing different VLA policies under single runtime, packaging each as a single self-contained GGUF that needs no Python or PyTorch.
- Profiled and tuned GPU execution using NVIDIA Nsight Systems and Nsight Compute, identifying CUDA kernel bottlenecks, memory bandwidth saturation, and SM occupancy issues across Jetson Orin and Thor compute profiles for cross-layer performance optimization.
Discover over 15,000 top freelancers
Statistics of experts using Streamlit
Aggregated from the professional profiles of matched freelancers.
Experience
12 years (Germany: 11 years)
Position duration
2 years (Germany: 1.8 years)
Positions per freelancer
12 (Germany: 9)
Top business areas
Information Technology, Research and Development, Business Intelligence
Top industries
Information Technology, Automotive, Education
Certification focus areas
Information Technology, Legal, Business Intelligence
Bachelor's degree or higher
100% (Germany: 99%)
Master's degree or higher
78% (Germany: 75%)
Doctorate
11% (Germany: 18%)
Certifications per freelancer
2
Most common languages
German, English, Spanish
Speak two or more languages
100%
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 Streamlit
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.
Streamlit 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 (90%)
- Automotive (70%)
- Education (60%)
- Healthcare (50%)
- Energy (40%)
- Media and Entertainment (40%)
- Professional Services (40%)
- Banking and Finance (30%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Streamlit does
Streamlit is an open-source Python framework for turning data scripts, analytical models and machine learning workflows into interactive web applications. It lets teams create usable interfaces without building a separate frontend stack, making results easier to explore, share and review.
Typical applications
Streamlit is used for internal tools, research interfaces and customer-facing prototypes. Common deliverables include:
- Interactive dashboards for operational or commercial data
- Interfaces for machine learning predictions and model review
- Data exploration tools for analysts and subject matter experts
- Prototypes that validate an idea before full product development
Ecosystem and tooling
Strong Streamlit work combines Python with libraries such as pandas, NumPy, Plotly, Altair and scikit-learn. Specialists also work with databases, REST APIs, notebooks, Git and cloud hosting, while authentication, secrets management and caching support reliable applications. Streamlit Community Cloud, Docker and enterprise deployment environments may all fit the delivery context.
When companies bring in experts
Companies often seek freelance expertise when a notebook needs a usable interface, a proof of concept must be demonstrated quickly, or an internal dashboard needs stronger structure and security. Munich teams may also value professionals who can collaborate on-site or remotely and communicate clearly in English or German. The right specialist can improve both the user journey and the underlying data flow.
Signs of strong expertise
Quality is more than making widgets appear on a page. Look for professionals who can:
- Structure reusable Python modules instead of one large script
- Design clear state, caching and error-handling patterns
- Connect data sources securely and validate inputs
- Explain model outputs and limitations to non-technical users
- Package, test and document an application for handover
Choosing the right specialist
Ask for examples of Streamlit applications with a similar data source, audience and deployment model. Discuss performance, access control, testing and maintenance before work begins. A strong professional can explain when Streamlit is appropriate, when a dedicated frontend is better, and how to move from a useful prototype to a dependable internal product.
Frequently asked questions
Quick answers to the questions that come up most around Streamlit.
Streamlit is mainly used to create interactive data applications from Python code. Companies use it for dashboards, model demonstrations, data exploration tools and internal workflows that need a clear interface without a large frontend project.
Streamlit is often faster to prototype because Python specialists can build the interface close to the data and model logic. Dash offers more granular dashboard control, while a custom frontend may be better for complex user journeys, broad public products or highly tailored visual design.
A strong Streamlit specialist usually understands Python, pandas, APIs, SQL and data visualisation. Experience with machine learning, cloud deployment, Docker, authentication and Git is also valuable when the application must move beyond a local prototype.
Streamlit projects vary from a focused prototype to a production application with several data sources and user roles. Choose a professional who has delivered work with a similar level of data complexity, deployment responsibility and audience rather than relying on framework familiarity alone.
Streamlit work is well suited to remote collaboration because code, data contracts and deployment workflows can be reviewed online. For Munich-based teams, on-site workshops can still help with requirements, stakeholder feedback and user testing, while day-to-day delivery remains remote.
Streamlit can support production use when the application has appropriate authentication, secrets handling, caching, testing, monitoring and deployment controls. It is a strong fit for many internal tools and analytical products, but a specialist should assess scale, concurrency and interface complexity before recommending it.
Review a Streamlit professional's examples for readable code, responsive interactions and clear handling of missing or invalid data. Ask how the work was tested, deployed and documented, and request an explanation of the trade-offs behind the chosen architecture.
A maintainable Streamlit dashboard separates presentation, data access and business logic. Clear state management, reusable components, controlled dependencies, documented configuration and an explicit deployment process make later changes safer and easier.
The average hourly rate of freelancers in Munich, Germany who have used Streamlit in their recent projects is 88 €, which corresponds to a daily rate of about 708 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Streamlit in their recent projects, 100% hold at least a Bachelor's degree, 78% hold at least a Master's degree, and 11% hold a doctorate.
On average, freelancers in Munich, Germany who have used Streamlit in their recent projects have 12 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 Streamlit in their recent projects are German (100%), English (100%), and Spanish (30%).
The most common industries among freelancers in Munich, Germany who have used Streamlit in their recent projects are Information Technology (90%), Automotive (70%), and Education (60%).
The most common business areas among freelancers in Munich, Germany who have used Streamlit in their recent projects are Information Technology (100%), Research and Development (90%), and Business Intelligence (80%).
Main locations of FRATCH Experts, who have recently used Streamlit
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 Request a free demo
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Philipp Thomaschewski
FRATCH CEO
