PyTorch Experts in Zurich
, matched in minutes from over 15,000 CVs with the power of AIHire experts who train deep learning models, design computer vision and natural language workflows, and productionise machine learning with Python, CUDA and TorchServe. FRATCH quickly matches you with vetted, available freelancers who fit your technical needs.
Meet FRATCH Experts in Zurich, 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.
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
24 years
Position duration
2.8 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
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 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Zurich 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 Zurich 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 (88%)
- Education (63%)
- Healthcare (50%)
- Insurance (38%)
- Manufacturing (38%)
- Professional Services (38%)
- Government and Administration (38%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Deep learning foundation
PyTorch is an open-source machine learning framework built around Python and dynamic computation graphs. It helps teams create, train and refine neural networks for research and production. Its tensor operations, automatic differentiation and GPU support suit demanding AI workloads.
Models and applications
PyTorch supports a wide range of systems, from image and video analysis to language models, recommendation engines and speech processing. Companies use it for experimentation as well as inference services embedded in products.
- Computer vision and image segmentation
- Natural language processing and generative AI
- Forecasting, ranking and recommendation models
- Reinforcement learning and simulation
Ecosystem and tooling
Professionals work across the wider PyTorch ecosystem, including TorchVision, TorchAudio, TorchText and TorchServe. They may also use Hugging Face Transformers, Lightning, ONNX, NVIDIA CUDA and cloud GPU services. Strong Python, data engineering and MLOps skills often complete the toolkit.
When specialists help
Freelance expertise is valuable when a team needs to turn a research model into a reliable product, accelerate training or assess an existing AI stack. In Zurich, this can support financial services, life sciences, robotics and industrial companies while enabling focused remote or on-site collaboration.
- Selecting an architecture and preparing training data
- Improving model quality, speed or memory use
- Moving notebooks into tested production pipelines
- Setting up monitoring, versioning and reproducible experiments
What strong professionals deliver
A strong specialist understands more than model code. They can explain trade-offs, build dependable data and evaluation flows, use profiling to find bottlenecks, and document how a model should be trained and served. They also distinguish genuine gains from results caused by leakage or weak validation.
Choosing the right fit
Look for evidence of shipped PyTorch systems that resemble your data, latency and deployment needs. Ask how the professional handled model evaluation, GPU costs, reproducibility, security and maintenance. Clear communication in English is common for distributed teams in Zurich; German can matter for some local stakeholders and documentation.
Frequently asked questions
Questions about PyTorch? Start with the answers below.
PyTorch is used to build and train neural networks for computer vision, language, speech, recommendation and forecasting systems. It supports fast experimentation in Python and can also run models in production through suitable serving and deployment tools.
PyTorch and TensorFlow both support deep learning research and production, but they differ in APIs, tooling and deployment preferences. PyTorch is often chosen for its Python-friendly eager execution and flexible debugging, while TensorFlow may fit teams already invested in its broader production ecosystem.
A strong PyTorch specialist usually brings Python, data preparation, SQL or data pipeline knowledge and experience with GPU acceleration. Depending on the project, Docker, Kubernetes, cloud services, model serving, experiment tracking and Hugging Face Transformers are also valuable.
The right PyTorch experience depends on the work, not simply on the framework itself. A prototype may need strong modeling and data skills, while a production system also requires testing, deployment, monitoring, performance tuning and an understanding of operational risks.
PyTorch work is often well suited to remote collaboration because code, experiments and cloud environments can be shared securely. On-site sessions in Zurich can still help with data access, stakeholder workshops, regulated environments or hardware integration.
Ask a PyTorch specialist to explain the data split, evaluation metrics, baseline models and failure cases behind a previous project. Quality work includes reproducible training, clear experiment tracking, sensible profiling and an honest account of where the model should not be trusted.
PyTorch can support production inference when the model is packaged, tested and served in an environment that meets its latency, reliability and security needs. Professionals may use TorchServe, ONNX or custom services, depending on the model and the target infrastructure.
A PyTorch freelancer should clarify the data rights, target hardware, model objective, evaluation method and expected handover before starting. It is also important to understand whether the assignment covers research only or includes data pipelines, deployment, monitoring and long-term maintenance.
The average hourly rate of freelancers in Zurich, 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 Zurich, 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 Zurich, Switzerland who have used PyTorch in their recent projects have 24 years of professional experience, with a single engagement typically lasting around 2.8 years.
The most common languages among freelancers in Zurich, Switzerland who have used PyTorch in their recent projects are English (100%), German (88%), and French (50%).
The most common industries among freelancers in Zurich, 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 Zurich, 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.
Countries:
Zurich 
Geneva Basel Bern Request a free demo
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