PyTorch Experts in Switzerland
matched in minutes from over 15,000 CVsHire experts who train and deploy deep learning models, build computer vision and natural language systems, and optimize PyTorch workflows with tools such as TorchVision, TorchText and TorchServe. FRATCH matches you quickly and precisely with vetted, available freelancers.
Meet FRATCH Experts in Switzerland, who have recently used PyTorch
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
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.
Christian B.
Last position:
Principal Business Acceleration & Advisory at Xebia
- Building productivity solutions for backoffice functions including case management and automated workflows in insurance
- Integration of GenAI functionality in operational processes for efficiency based on measurable KPIs
- Successfully delivering integration and data projects with agile methodology in requirements engineering, cloud native development and computing, software development, data analytics, quality management and technical documentation
- Coaching and advising clients for adoption of technologies such as use case identification, organizational impact assessment and building a business case for investments
- Advising clients on cost optimization through IT carve out and outsourcing of software product engineering
- Technologies: Jira, Confluence, ServiceNow, GitHub Co-Pilot, Agile/SAFe
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
3 years
Positions per freelancer
10
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
88%
Doctorate
13%
Certifications per freelancer
3
Most common languages
English, German, French
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 Switzerland 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 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 19 Sep 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 (88%)
- Education (63%)
- Healthcare (38%)
- Insurance (38%)
- Professional Services (38%)
- Government and Administration (38%)
- Telecommunication (38%)
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 deep learning models. Its Python-first interface, dynamic computation graphs and tensor operations make experimentation practical while supporting production workloads. Companies use it for computer vision, natural language processing, recommendation systems, speech, generative AI and scientific computing.
Core capabilities
PyTorch specialists work across the full model lifecycle, from data preparation and architecture design to evaluation and deployment. Typical deliverables include:
- Image classification, detection and segmentation models
- Language, embedding and retrieval systems
- Training pipelines with reproducible experiments
- Fine-tuning and evaluation for foundation models
- Inference services for applications and internal tools
Ecosystem and tooling
The framework connects with NumPy, pandas, scikit-learn and the wider Python data ecosystem. Specialists may use TorchVision, TorchAudio, TorchText, TorchScript, TorchServe and distributed training utilities, alongside CUDA and GPU libraries. Hugging Face Transformers, MLflow, Docker and cloud infrastructure often complete the workflow.
When companies need specialists
Freelance expertise helps when a team must turn research into a reliable product, adapt an existing model or improve an expensive training process. It is also useful when internal teams need a clear evaluation strategy, production monitoring or support for GPU-based workloads. In Switzerland, remote collaboration can work well across technical teams, while on-site workshops may help with sensitive data, product decisions or stakeholder alignment.
What strong professionals deliver
Strong PyTorch professionals understand both model quality and the system around it. They define suitable metrics, manage datasets carefully, track experiments and explain trade-offs between accuracy, latency, cost and maintainability. They write testable code, document assumptions and make inference behavior reproducible across development and production environments.
Choosing the right fit
Look for evidence of work similar to your data type, model family and deployment target. Ask how the specialist handles data leakage, class imbalance, model drift, reproducibility and failure cases. Relevant experience with Python, SQL, APIs, containers, cloud GPUs and MLOps is valuable, but the right combination depends on whether the project is exploratory research, model adaptation or a customer-facing system.
Frequently asked questions
Need clarity? These are the questions we hear most often about PyTorch.
PyTorch is used to create and train neural networks for computer vision, language processing, speech, recommendations and generative AI. Companies also use it for scientific models, rapid experimentation and production inference services.
PyTorch and TensorFlow both support deep learning, GPU acceleration and production deployment. PyTorch is often chosen for its Pythonic workflow and flexible experimentation, while the best choice depends on the existing stack, team skills, deployment constraints and model ecosystem.
A strong PyTorch specialist often brings Python, data processing, SQL, APIs and containerization skills. Experience with CUDA, cloud GPU services, experiment tracking, model serving and MLOps is especially useful when a model must run reliably beyond a notebook.
The required background depends on the task. A contained model adaptation may need a specialist who can work within an established pipeline, while a new training and deployment system calls for broader expertise in data quality, evaluation, infrastructure and operational support.
PyTorch projects are often suitable for remote collaboration when data access, compute environments and documentation are well organized. On-site work can still be useful for workshops, regulated data handling, product discovery or close coordination with teams in Switzerland.
A PyTorch freelancer should usually provide reproducible training code, data and evaluation documentation, model artifacts and a clear inference process. Depending on the project, the deliverables may also include an API, container setup, monitoring guidance and handover sessions.
Review whether the specialist can explain dataset choices, baselines, metrics and error analysis in clear terms. Good PyTorch work includes reproducible experiments, appropriate validation, tested inference code and an honest account of limitations rather than only a strong headline metric.
PyTorch grew from the earlier Torch ecosystem and kept elements of its name and tensor-focused approach. It is a distinct framework built around Python and dynamic computation graphs, so experience with Torch can be relevant but does not replace hands-on PyTorch knowledge.
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 823 € 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, 88% hold at least a Master's degree, and 13% 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 3 years.
The most common languages among freelancers in Switzerland who have used PyTorch in their recent projects are English (100%), German (88%), and French (50%).
The most common industries among freelancers in Switzerland who have used PyTorch in their recent projects are Information Technology (100%), Banking and Finance (88%), and Education (63%).
The most common business areas among freelancers in Switzerland who have used PyTorch in their recent projects are Information Technology (100%), Product Development (88%), and Research and Development (63%).
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
