Streamlit Experts in Munich
matched in minutes by AIHire experts who turn Python data workflows into interactive web apps, analytical dashboards and machine learning demos with Streamlit, pandas and Plotly. FRATCH connects you with vetted, available freelancers through fast, precise AI matching.
Meet FRATCH Experts in Munich, who have recently used Streamlit
Hachem S.
Last position:
Machine Learning & BI Lecturer at EU Business School
- Taught ML, BI, and Big Data to Master's students.
Tech: Python, Power BI, SQL, ML, SnowflakeDB, databricks, MS Fabric, MongoDB, LLM
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
Serge K.
Last position:
Data Scientist & MLOps (Machine Learning Operations) at REWE Digital GmbH
- It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
- GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
- Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
- CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment
- Development of feature store and data synchronization between environments
- Data science: evaluation of different forecasting models, including CatBoost, Contextual Gamma and Random Forest; implementation of training pipelines using Kubeflow on Vertex AI; scaling and development of an event-driven interface using Kafka as a forecasting service; evaluation of MLFlow for model tracking and model registry
- Implementation of REST API using KeyCloak OAuth2 and OpenID (JWT)
- Monitoring: implementation of dashboards with Grafana, Prometheus, Mimir, Streamlit and Loki to monitor prediction metrics, GCP and Snowflake costs for pipelines, and system metrics
- Development of Copilot Agent Skills
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
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
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.
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.
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.
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:
Senior AI Engineer and Technical Advisor (Freelancer) at dialpad
Serving as AI technical advisor to Dialpad's Telephony Engineering division while building AI products hands-on. Currently developing MCP-based tooling for AI text and voice agents: agent authoring, integrations and adoption of existing agents, shared authentication and API contracts, and durable workflow state. This includes text and voice tests that evaluate agent behavior, latency and task completion.
In parallel, helping Dialpad's engineers adopt QualityLayer and use Claude Code and Codex effectively. This ongoing work covers context management for large codebases, reusable plan-implement-test-review workflows connected to CI/CD, playbooks, hands-on workshops, labs and pair programming.
Technologies: AI Agent Products (Text & Voice Agents, MCP, LLMs, Python, TypeScript), Agent Tooling (Authoring, Integrations, OAuth, Durable State), Quality & Evaluation (Text & Voice Tests, Evals, Regression, Traces), Agentic Engineering (Claude Code, Codex, QualityLayer, Hooks, Prompts), Context Engineering (RAG, Vector Stores, Codebase Embeddings), Software Delivery (CI/CD Integration, Agentic Loops, PR Automation), Enablement & Coaching (Workshops, Hands-On Labs, Pair Programming)
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
1.8 years (Germany: 1.7 years)
Positions per freelancer
12 (Germany: 9)
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Automotive, Education
Certification focus areas
Information Technology, Business Intelligence, Legal
Bachelor's degree or higher
100%
Master's degree or higher
82% (Germany: 79%)
Doctorate
18% (Germany: 20%)
Certifications per freelancer
2
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Munich are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging 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 29 Oct 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 (92%)
- Automotive (67%)
- Education (67%)
- Healthcare (50%)
- Media and Entertainment (42%)
- Professional Services (42%)
- Energy (33%)
- Retail (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Python Data Apps
Streamlit is an open-source Python framework for turning data scripts into interactive web applications. Professionals use it to expose filters, charts, tables, forms and model outputs through a browser without building a separate frontend. It suits internal tools, analytical interfaces, prototypes and focused customer-facing applications where speed and clarity matter.
Typical Projects
- Build dashboards for sales, operations, finance or research teams
- Create interfaces for machine learning models and data workflows
- Turn notebooks and Python scripts into shareable applications
- Add file uploads, filtering, authentication and export features
- Present live data through APIs, databases or scheduled pipelines
Streamlit projects often begin as proof of concept and grow into maintained business tools. A strong specialist keeps the app useful as its data sources, users and requirements become more demanding.
Tools Around It
Streamlit works closely with Python libraries such as pandas, NumPy, scikit-learn and PyTorch. Plotly, Altair and Matplotlib support interactive or presentation-ready visualisations, while SQL, REST APIs and cloud storage connect the application to operational data. Professionals also work with Git, testing tools, Docker and deployment services such as Streamlit Community Cloud or managed cloud environments.
When Expertise Helps
Companies bring in freelance Streamlit expertise when a data team has a working script but lacks a reliable interface. They may need help structuring session state, improving performance, securing sensitive data or moving an experiment into a repeatable deployment process. In Munich, collaboration can combine remote delivery with on-site workshops, especially for teams coordinating product, analytics and business stakeholders.
Delivery Skills
Good professionals define the user journey before adding widgets. They separate data preparation, business logic and presentation, manage caching carefully, handle errors clearly and make layouts usable on different screens. They also understand data permissions, secrets management, dependency control and deployment so that a Streamlit app remains dependable beyond its first demonstration.
Choosing a Specialist
Look for evidence of complete Streamlit applications rather than isolated charts or notebook screenshots. Ask how the professional handled slow queries, changing data, authentication, testing and handover. Relevant adjacent experience includes Python packaging, SQL, cloud infrastructure, machine learning operations and visual analytics. For German organisations, clear communication in English or German can support workshops, documentation and ongoing remote collaboration.
Frequently asked questions
Quick answers to the questions that come up most around Streamlit.
Streamlit is used to create interactive web applications from Python code, especially for data exploration, dashboards, machine learning demonstrations and internal business tools. It lets users interact with filters, charts, tables and model inputs without needing a separate frontend stack.
Streamlit usually enables faster delivery for Python-led data applications because the interface stays close to the analysis code. Dash offers more structured control over callbacks and layouts, while a custom frontend is better when a product needs highly tailored interaction, branding or complex client-side behaviour.
A strong Streamlit professional should also understand Python, pandas, SQL and data visualisation. Experience with APIs, authentication, testing, Docker, cloud deployment and machine learning workflows is valuable when the application must connect to real systems.
The right level of Streamlit experience depends on the scope, data sensitivity and deployment needs. A simple internal prototype may need focused framework knowledge, while a production application calls for deeper skills in architecture, performance, security, testing and operations.
Streamlit is well suited to remote collaboration because applications, requirements and review environments can be shared through Git and cloud deployment. Munich-based teams may still prefer on-site discovery sessions, while implementation, testing and handover can usually happen remotely with clear documentation.
Review a working Streamlit application and ask the specialist to explain its data flow, caching, error handling and deployment setup. Strong professionals can describe trade-offs, show maintainable code and demonstrate how they protect secrets and sensitive data.
Streamlit can support production use when its application design, access controls, data connections and deployment environment are planned properly. It is a strong fit for many internal and analytical tools, but a specialist should assess traffic, interaction complexity and operational requirements before recommending it for a public product.
A Streamlit assignment often involves more than arranging widgets on a page. Freelancers should clarify the target users, data sources, refresh needs, authentication, hosting, ownership of the code and the expected path from prototype to maintained application.
The average hourly rate of freelancers in Munich, Germany who have used Streamlit in their recent projects is 89 €, which corresponds to a daily rate of about 716 € 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, 82% hold at least a Master's degree, and 18% 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 1.8 years.
The most common languages among freelancers in Munich, Germany who have used Streamlit in their recent projects are German (100%), English (100%), and French (42%).
The most common industries among freelancers in Munich, Germany who have used Streamlit in their recent projects are Information Technology (92%), Automotive (67%), and Education (67%).
The most common business areas among freelancers in Munich, Germany who have used Streamlit in their recent projects are Information Technology (100%), Business Intelligence (83%), and Product Development (83%).
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.
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