PyTorch Experts in Switzerland
matched in minutes from over 15,000 CVs with the power of AIHire experts who train deep learning models, build computer vision systems and deploy machine learning services with PyTorch, supported by the wider Python ecosystem. FRATCH matches you quickly and precisely with vetted, available freelancers.
Meet FRATCH Experts in Switzerland, who have recently used PyTorch
Mohamad K.
Last position:
Senior Backend Developer at Standing on Giants
- Led architecture and end-to-end engineering delivery for community-driven SaaS platforms serving 2M+ monthly active users.
- Architected and led the migration of a monolithic Python/FastAPI and PostgreSQL database and LangChain with codebase to an event-driven microservices architecture on AWS EKS, sustaining 10x traffic growth from ~150 RPS to 1,500+ RPS with zero re-architecture cycles.
- Defined and enforced engineering standards across services including API contracts, observability baselines, and deployment topology, reducing production incidents by 55% and MTTR from 2 hours to under 25 minutes within 9 months.
- Redesigned the caching and query layer using multi-tier Redis caching and database indexing/partitioning, cutting p95 API latency from 850ms to 180ms (78% reduction) and database CPU load by 45%.
- Built CI/CD platform on GitHub Actions, Terraform, and Kubernetes (EKS) with blue-green and canary rollouts, increasing deployment frequency from ~2/month to 8-12/day and reducing lead time from 10 days to under 6 hours.
- Implemented contract testing, automated load testing, and observability SLOs using Prometheus, Grafana, and OpenTelemetry, raising platform availability from 99.5% to 99.95% (10x reduction in error budget burn).
- Led and grew a cross-functional team of 8 engineers across backend, frontend, and DevOps, scaling headcount from 4 to 8 with 85% retention; owned hiring, onboarding, performance reviews, and career development.
- Partnered with Product, Design, and Client Success leadership as primary technical decision-maker; translated business goals into technical roadmaps and drove build-vs-buy decisions on authentication, search, and AI tooling.
- Introduced AI-assisted development workflows including automated code review and a RAG-based internal knowledge assistant using Graph (GraphRAG, Neo4J), increasing sprint throughput by 30% across two quarters.
- Owned incident command and production support rotation; established runbooks, postmortem culture, and on-call SLOs, reducing weekend paging incidents by 70%.
- Developed and optimized Algorithms using python libraries like Numpy and Pandas.
Malik O.
Last position:
AI Scientist at SQPS
- Intelligent Document Processing Engine with OCR & Computer Vision: Architected the end-to-end AI pipeline at the core of the SQPS GenAI document-processing product, which turns scanned paper and PDF batches into organised, searchable documents, combining image pre-processing, OCR and layout analysis for mixed-quality scans. Technologies: Python, OpenCV, Tesseract / PaddleOCR, PyTorch, FastAPI
- Automatic Document Splitting & Classification: Developed models that detect where one document ends and the next begins inside a single multi-document scan, and classify each one by type (invoices, contracts, letters, reports) to enable fully automated filing. Technologies: Python, PyTorch, scikit-learn, Hugging Face Transformers
- Information Extraction & Intelligent File Naming with LLMs: Implemented an LLM-based GenAI extraction system that reads dates, names and reference numbers from unstructured documents and uses them to generate consistent, rule-based file names, with configurable naming conventions per organisation. Technologies: Python, LangChain, OpenAI, SentenceTransformers, SQL
Christian B.
Last position:
5G Campus Product Advisor Telco at German railway group
- Definition of a product concept and end to end solution delivery (turnkey) for a Digital Efficiency Hub for Railway Maintenance based on 5G campus networks. Integration of various hardware and software systems in a comprehensive delivery with success metrics. Laise the concept and process across the enterprise. Compliant process definition and financial management with commercial model.
Technologies: 5G, RAN, SAP, Cloud, Enterprise Architect, Autonomous Robots, ITIL, CoBIT, TBM, BSI, ISO 27001
Gwang Jin K.
Last position:
Data Scientist / Applied AI, Automation & Data Systems Researcher at Independent
- Built and explored applied GenAI, RAG, GraphRAG, local LLM, agentic AI and document-intelligence prototypes for structured analysis, evidence extraction, semantic search, technical reasoning and decision-useful reporting
- Developed private local-LLM workflows and AI system patterns focused on privacy, reproducibility, reviewability, low-cost inference and practical user control
- Built reproducible Python/R workflows for data analysis, automation, API-driven tooling, validation logic, technical documentation and AI-assisted software development
- Designed workflows around explicit assumptions, traceable inputs, reviewable outputs and failure-mode awareness rather than black-box “looks good” demonstrations
- Supported RAHN AG in a chemical/regulatory environment with data extraction and processing around WERCS, a regulatory application for chemical product and compliance data
- Explored complex application/database schemas and wrote nested SQL queries to extract information for mixture calculations, component relationships, regulatory rules and reporting logic
- Continued hands-on development in Git/GitHub/GitLab/Bitbucket, Docker/Linux deployment patterns, REST/API workflows, error handling, technical writing and fast AI-assisted prototyping
- Built technical writing and documentation workflows that turn complex systems into clear runbooks, checklists, decision notes and user-facing explanations
Ursula M.
Last position:
Business Mentor at RoleModel Rebels
- Mentor female students and professionals in advancing their careers, particularly as aspiring tech entrepreneurs.
Matthias I.
Last position:
Fractional CTO (Principal Engineer / Technical Architect)
- Designed large-scale systems and APIs serving thousands of concurrent users.
- Refactored a 650k-LOC monolith and led full AWS migration for stable performance.
- Introduced SLO-based observability, improving reliability and recovery flow.
- Optimised cloud and databases, achieving significant cost and latency reduction.
- Delivered LLM, RAG, and document-automation pipelines adopted in production.
Alejandro A.
Last position:
AI Researcher & Engineer at Tufa Labs
- Deployed and optimized the inference stack on a multi-node DGX B200 cluster across vLLM and SGLang (serving, throughput and latency tuning).
- Built, with a small team, an internal Python library for LM pretraining covering the full training loop: distributed training with PyTorch FSDP, data pipelines, checkpointing, config and hyperparameter management, and experiment tracking.
- Built and evaluated agent scaffolds on interactive game benchmarks similar to ARC-AGI-3, with metrics for how models plan, explore and adapt across multi-step episodes; classified model errors and fed the findings back into scaffold and evaluation design.
- Researched looped transformer architectures.
Karl E.
Last position:
incl. CI/CD, automation at AALS Software AG
- Designed and delivered a practical real-time course on Flink and Hadoop with MapReduce, HDFS, Spark, Flink, Hive, HBase, MongoDB, Cassandra, and Kafka
- Gained extensive DevOps and CI/CD experience
- Created ETL/ELT pipelines with Apache tools and Pentaho
- Led projects in municipal software, financial services, and big data with Kafka
- Developed AI/NLP models and chatbots with RASA, Chatter, and Dialogflow
- Built and managed a TypeDB knowledge database
- Worked with OpenStack, Kubernetes, and Podman
Nenad T.
Last position:
Contractor at Swisscom
- SAP Data Designer ETL, SQL Server, Docker for PharmaSuisse Data Warehouse Application (Application Manager and Developer)
- SAP Business Objects, ABAP on SAP HANA for the Police of the Canton of Bern (Application Manager and Developer)
- Avaloq Connectors to SAP BW for Banque Cantonale de Fribourg
Discover over 15,000 top freelancers
Statistics of experts using PyTorch
Aggregated from the professional profiles of matched freelancers.
Experience
23 years
Position duration
2.6 years
Positions per freelancer
11
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Banking and Finance, Education
Certification focus areas
Information Technology, Research and Development, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
89%
Doctorate
22%
Certifications per freelancer
5
Most common languages
English, German, 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 Switzerland 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 Switzerland using PyTorch
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.
PyTorch 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 (100%)
- Banking and Finance (78%)
- Education (67%)
- Healthcare (56%)
- Manufacturing (44%)
- Insurance (33%)
- Professional Services (33%)
- Government and Administration (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What PyTorch does
PyTorch is an open-source machine learning framework for building, training and deploying neural networks. Its Python-first design, dynamic computation graphs and automatic differentiation make it practical for research and production. Teams use it for language models, recommendation systems, image analysis, speech processing and generative AI.
Core capabilities
PyTorch specialists work across the full model lifecycle:
- Design datasets, tensors and training pipelines
- Build and fine-tune deep learning models
- Optimise GPU and distributed training
- Evaluate accuracy, robustness and bias
- Package models for production inference
They also select suitable loss functions, optimisers and evaluation methods for the business problem rather than treating model training as an isolated task.
Ecosystem and tooling
The ecosystem includes TorchVision for computer vision, TorchAudio for audio workloads and TorchText-compatible workflows for language tasks. Professionals often combine PyTorch with Hugging Face Transformers, NumPy, pandas, scikit-learn and experiment tracking tools. They may use CUDA, Docker, Kubernetes and cloud GPU services to move models from notebooks into reliable services.
When companies need specialists
Freelance expertise is useful when a team needs to turn a research prototype into a tested product, adapt an existing model or control demanding compute costs. In Switzerland, PyTorch work can support financial services, life sciences, robotics, manufacturing and other data-intensive industries. Remote collaboration works well when repositories, experiments and deployment environments are documented clearly.
Typical deliverables
A PyTorch engagement may produce a cleaned dataset, reproducible training code, a tuned model, evaluation reports and an inference API. Other deliverables include model monitoring, batch prediction jobs, GPU deployment and technical documentation. Strong professionals connect each deliverable to latency, privacy, maintainability and measurable product needs.
Signs of strong expertise
Look for specialists who can explain design choices and reproduce their results, not only present a high score:
- They separate training, validation and test data correctly
- They profile memory use and GPU performance
- They manage checkpoints, experiment versions and dependencies
- They test models against realistic edge cases
- They understand deployment constraints and monitoring
Experience with Python software engineering, data pipelines, APIs and cloud infrastructure is often as important as knowledge of neural network layers.
Frequently asked questions
Need clarity? These are the questions we hear most often about PyTorch.
PyTorch is used to create and train deep learning models for computer vision, natural language processing, speech, recommendation and generative AI. Companies also use it to export models and serve predictions inside production applications.
PyTorch and TensorFlow both support production machine learning, GPU acceleration and distributed training. PyTorch is often preferred for its Pythonic workflow and flexible experimentation, while the right choice depends on existing systems, deployment targets and the team's expertise.
A strong PyTorch specialist usually brings Python, data preparation, SQL and software testing skills. Depending on the project, experience with Hugging Face, CUDA, Docker, cloud GPUs, APIs, Kubernetes and model monitoring is also valuable.
PyTorch project needs vary with the deliverable. A focused model integration may suit a specialist familiar with established architectures, while new training methods, distributed workloads or regulated applications require deeper expertise in experimentation, evaluation and production controls.
PyTorch work is often suitable for remote collaboration because code, datasets, experiments and cloud environments can be shared securely. For Swiss teams, on-site workshops may still help with domain discovery, data access and coordination with product or research groups.
PyTorch is a good fit when a company needs custom neural network architectures, transfer learning, fine-tuning or control over training and inference. For tabular prediction with limited data, a simpler library may be faster to maintain and easier to explain.
Review how a PyTorch specialist handles data splits, baselines, reproducibility and failure cases. Ask for clear evaluation against a business-relevant metric, evidence of performance profiling and an explanation of how the model will be monitored after release.
A PyTorch freelancer should expect close work with product, data and infrastructure professionals rather than model training in isolation. Clear communication in English is commonly useful, while German, French or Italian may matter depending on the Swiss stakeholders and project location.
The average hourly rate of freelancers in Switzerland who have used PyTorch in their recent projects is 103 €, which corresponds to a daily rate of about 822 € based on an 8-hour working day.
Of the freelancers in Switzerland who have used PyTorch in their recent projects, 100% hold at least a Bachelor's degree, 89% hold at least a Master's degree, and 22% hold a doctorate.
On average, freelancers in Switzerland who have used PyTorch in their recent projects have 23 years of professional experience, with a single engagement typically lasting around 2.6 years.
The most common languages among freelancers in Switzerland who have used PyTorch in their recent projects are English (100%), German (89%), and French (56%).
The most common industries among freelancers in Switzerland who have used PyTorch in their recent projects are Information Technology (100%), Banking and Finance (78%), and Education (67%).
The most common business areas among freelancers in Switzerland who have used PyTorch in their recent projects are Information Technology (100%), Product Development (89%), and Research and Development (67%).
Main locations of FRATCH Experts, who have recently used PyTorch
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.
Zurich Request a free demo
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Philipp Thomaschewski
FRATCH CEO
