Streamlit Experts in Munich
in minutes from over 15,000 CVs with the power of AI.Hire experts who turn Python data work into fast dashboards, internal tools, and interactive model demos with Streamlit. Get precise matching with vetted, available specialists who can move from concept to working app quickly.
Meet FRATCH Experts in Munich, who have recently used Streamlit
Philipp Grunert
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
Mirza Klimenta
Last position:
Agentic AI for a DeepResearch project at Freelance
- Created a multi-agentic system supported by a knowledge graph to automate drafting of research papers
- Used multiple experts (OpenAI models) collaborating during document drafting
- Extracted useful information from the knowledge graph
- Technologies: LangChain, LangGraph, Smolagents, LlamaIndex, dspy
- Infrastructure: Terraform and GitHub Actions (CI/CD) on AWS
- Deployed initial application as a Streamlit app
Christian Schulz
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 Heinemann
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 Peñafiel Palmer
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.
Max Ritter
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 Gmbh
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
Heidi Albarazi
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
Azada Henze
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.
Adithya Balaji
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
2.1 years (Germany: 1.8 years)
Positions per freelancer
12 (Germany: 8)
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: 98%)
Master's degree or higher
78% (Germany: 75%)
Doctorate
11% (Germany: 17%)
Certifications per freelancer
2
Most common languages
German, English, Spanish
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What Streamlit does
Streamlit turns Python code into interactive web apps with very little front-end work. Teams use it to share data views, test ideas with stakeholders, and build quick internal tools that feel polished without a full product stack.
Typical uses
- Data dashboards for business and product teams
- Model demos for ML and data science projects
- Internal tools for reviewing, filtering, and exploring data
- Prototype apps for early validation with users
The ecosystem
Strong Streamlit specialists work comfortably with Python, pandas, NumPy, Plotly, Altair, and common data sources. They also know how to structure app state, handle forms, and manage deployment details so the app stays stable as usage grows.
When to bring in help
Companies usually bring in freelance Streamlit expertise when a prototype needs to become usable fast, when an analyst team needs a clean internal app, or when a model needs a simple interface. In Munich, this often suits teams that want close collaboration with data, product, or research groups while keeping development lean.
What good specialists deliver
Strong professionals do more than place widgets on a page. They design clear workflows, keep code readable, separate data logic from presentation, and make sure the app is easy to update when the underlying dataset or model changes.
Collaboration and quality
Remote work fits Streamlit well because the work is usually Python-based and easy to review in shared repos. Good specialists document setup, clarify data assumptions, and deliver apps that are simple to run, test, and extend for the next round of features.
Frequently asked questions
Quick answers to the questions that come up most around Streamlit.
Streamlit is used to turn Python scripts into interactive apps for data exploration, dashboards, and model demos. Companies often choose it when they need a clear interface without building a separate front end. It works well for internal tools and quick prototypes that still need to feel clean and reliable.
You should bring in Streamlit expertise when a data idea needs to become usable quickly, or when an existing prototype needs structure and polish. Freelancers are especially useful when your team has Python skills but not enough time to design the app flow and deployment. They can also help when an internal tool must be maintained by non-specialists.
Streamlit is usually faster to start with and simpler to read, which makes it attractive for data-focused apps and prototypes. Dash often gives more control over app structure and component behavior, but it can take more setup. The right choice depends on whether speed of delivery or deeper UI control matters more.
A strong Streamlit specialist usually knows Python well and can work with pandas, NumPy, and common charting tools like Plotly or Altair. It also helps if they understand SQL, APIs, and basic deployment. For machine learning projects, experience with model packaging and inference flows is a plus.
A simple Streamlit app may only need a specialist who can build clean layouts, connect data, and handle deployment. More complex work needs someone who can manage session state, permissions, performance, and reusable components. If the app will support real business decisions, quality and maintainability matter as much as speed.
Yes, Streamlit is a good fit for remote work because it is code-first and easy to review in version control. Teams can share changes, test the app in staging, and refine the data flow without long handover cycles. That said, on-site workshops in Munich can help when the app needs close input from business users or analysts.
A good Streamlit deliverable is clear, stable, and easy to maintain. Look for readable code, sensible app structure, good handling of empty or broken data, and a UI that supports the actual task instead of just showing charts. The best specialists also explain trade-offs and leave the project in a state your team can extend.
No, Streamlit is often used for prototypes, but it can also support useful internal tools and lightweight production apps. The key question is whether the app needs simple interaction around Python data workflows or a full custom product interface. For many teams, it is the fastest way to move from analysis to something people can actually use.
The average hourly rate of freelancers in Munich, Germany who have used Streamlit in their recent projects is 90 €, which corresponds to a daily rate of about 721 € 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 2.1 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
