Machine Learning Experts in Switzerland
, matched in minutes by AI from over 15,000 CVsHire experts who train predictive models, design recommendation systems and productionize computer vision or natural language solutions. FRATCH connects you with vetted, available freelancers through fast, precise AI matching.
Meet FRATCH Experts in Switzerland, who have recently used Machine Learning
Mike B.
Last position:
Freelance UX-Researcher, Designer at Bodensee Schiffsbetriebe; Labhard-Medien
- UX research, UX concept development, and UX design for an AI dialogue system with information on events, locations, public transport timetables, and special trips. Observations, remote user tests, session recordings, analytics evaluations, RapidUserTests, prototyping, etc., using Figma/Figma Make, Claude Design, and OpenAI Codex (Design)
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
Banashankari N.
Last position:
Senior IT Project Manager, Data and Integration Platform at MCH Group, Group IT
Led implementation of an Azure cloud-based, event-driven enterprise integration platform connecting Salesforce, Momentus(event) and ERP applications. Reduced overall costs by 60%, lowered operational errors to under 1%, and delivered on time.
- Led the full project lifecycle from initiation to delivery using agile and hybrid methods - platform vision, feasibility, technical blueprint, architecture, implementation, integration, testing, rollout and business adoption.
- Built the engineering team from the ground up, leading teams across Switzerland, Bulgaria and India using Scrum and Kanban; established Jira, Confluence, Asana and SharePoint for delivery and reporting.
- Established and managed frequent steering meetings, reporting that gave stakeholders transparency and drove key decision-making.
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.
Andreas I.
Last position:
Business Development at SoftQuadrat GmbH
- Analysis of the current situation and repositioning of the brand
- Building up recruiting, sales, and customer support
- Planning for trade fair participation
- Establishing a sales process
- Introducing a CRM tool (Pipedrive)
- Setting up a subsidiary in Switzerland
- Implementing all aspects of temporary staffing
Niamh M.
Last position:
Paid Services Consultant (Multi-Channel) at WPP Media
- Lead consultant for SEA campaigns with a senior focus on performance strategy, while managing multi-channel media campaigns across DV360, Meta, TikTok, and YouTube
- AI Marketing: WPP Media State of the Art AI technology
- Collaborate closely with planning and analytics teams to ensure cross-channel consistency, including audience overlap analysis across online video and CTV
- Manage accounts for large European / global brands such as MediaMarkt, Universal Pictures, Emmi, and Nestlé
Stefan B.
Last position:
Co-Founder at Stealth AI Infrastructure Deep-Tech Venture
- Deep-tech venture in enterprise AI model compression for on-premises and edge deployment.
- Built systematic R&D pipeline, business and technical architecture, early investor pipeline, and design partner network across AI verticals, datacentre operators, and robotics/edge computing — from zero.
- Ventures built concurrently, each in a fundamentally different regulatory and technical domain.
Ursula M.
Last position:
Business Mentor at RoleModel Rebels
- Mentor female students and professionals in advancing their careers, particularly as aspiring tech entrepreneurs.
Patrick R.
Last position:
DevOps Engineer at Swisscard AECS AG
- PoC for various platform integration functionalities using Backstage and Crossplane
- Implementation of improvements of developer experience with Azure AVD and DevSpace
- Manage application deployment infrastructure on Kubernetes clusters
- Manage CI/CD platform
- Manage deployments into Kubernetes (Rancher and AKS) and into Azure
- Act as security champion of the DevOps team
- Manage vulnerabilities and support dev teams to manage third-party vulnerabilities using JFrog Xray
- Support building the platform team
- Manage infrastructure as code using Terraform and Atlantis
- Build and integrate a platform and IDP using Crossplane
Fabian K.
Last position:
Lecturer at HWZ University of Applied Sciences
- Co-teach in CAS AI Management and CAS AI Innovation programs for future AI managers
- Cover topics including data platforms, AI architecture, technology adoption foundations, and factors influencing enterprise AI initiative success
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.
Andrew L.
Last position:
Service Manager - Testing at Takeda Pharmaceutical International AG
- Managing the Testing services for the Global IT Testing Centre of Excellence (TCoE) team, which is responsible for managing and supporting the Product & Project Testing Workstreams on various Regional Projects, especially in Pharmacovigilance, Digital Supply Chain, Clinical Practice and R&D, etc.
- Managing Client relationships, escalations and continuing to grow the project services globally, while also driving the testing efforts on Takeda’s AI, DevOps, Agile, and Digital projects.
- Led the implementation of a Quality Management System (QMS), ensuring compliance with GCP, GMP, and Swiss and EU regulations, resulting in a huge improvement in audit readiness.
- Overseeing projects for systems including ERP, EDGE, SAP ECC, SAP Transportation Management, S/4 HANA migration etc.
- Led an AI/ML PoC for AI implementation.
- Led a cross-functional team to ensure compliance with MDR, FDA regulatory guidelines and achieved successful project deliveries.
- Led and managed change management and communication for TCoE, keeping the Takeda organisation informed of change communication and practices, including presentations to various stakeholders.
- Ensuring TCoE outsource vendor resources adhere to regulatory, compliance and quality system standards and practices for GxP & non-GxP System Development Life Cycle (SDLC) including aSDLC and TCoE Global Standards.
- Translated the product vision, strategy and requirements into backlog items, which were prioritised based on potential business impact and customer value.
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.
Zafer B.
Last position:
Lead Test Automation Engineer at Unknown
- Playwright-based API test automation for the Oracle JD Edwards ERP system
- Development of a TypeScript/Node.js test framework with MSSQL database integration
- End-to-end business process automation for order processing, customer management, etc.
- OpenAI integration for automatic PDF document validation
- Jira/Xray integration for automated test reporting with dashboard
- REST API client development with role-based authentication
- Test data management, dynamic ID validation, and cross-test data sharing mechanisms
Ala L.
Last position:
VR/AR/ML Project Site Lead (contract by Experis) at Meta
- Acted as project lead in different internal projects, including the development and implementation of innovative solutions based on machine learning, virtual and augmented reality with the aim of providing great user experience
- Drove project planning, execution and reporting, designed risk mitigation and schedule adjustment plans to bring the projects on the green path
- Directed the process optimization and conducted project reviews by being the liaison between engineering teams and executive stakeholders
- Served as agile coach and led the scrum ceremonies such as daily stand-ups, sprint planning, sprint review and sprint retrospective
Discover over 15,000 top freelancers
Statistics of experts using Machine Learning
Aggregated from the professional profiles of matched freelancers.
Experience
24 years
Position duration
2.5 years
Positions per freelancer
10
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Banking and Finance, Education
Certification focus areas
Information Technology, Project Management, Marketing
Bachelor's degree or higher
94%
Master's degree or higher
72%
Doctorate
17%
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 Machine Learning
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.
Machine Learning 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 (85%)
- Banking and Finance (80%)
- Education (50%)
- Professional Services (50%)
- Media and Entertainment (35%)
- Telecommunication (35%)
- Healthcare (30%)
- Insurance (30%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Machine Learning does
Machine Learning enables software to learn patterns from data and produce predictions, classifications, recommendations or generated content. It supports fraud detection, demand forecasting, search, personalization, image analysis and language-based products. Unlike fixed rule systems, models can improve as relevant data and feedback become available.
Models and methods
Professionals select methods based on the data, target outcome and operational constraints. Common approaches include supervised and unsupervised learning, deep learning, reinforcement learning, time-series forecasting and anomaly detection. Strong work starts with a clear problem definition, a reliable target and an evaluation method that reflects real business use.
Tools and ecosystem
The ecosystem spans Python, SQL, notebooks and data processing frameworks, with libraries such as scikit-learn, PyTorch, TensorFlow and XGBoost. Specialists also work with feature stores, experiment tracking, model registries, vector databases and cloud services. Production delivery may involve Docker, Kubernetes, APIs, batch pipelines and monitoring systems.
Where companies use it
- Forecast demand, revenue, capacity or maintenance needs
- Rank products, search results and personalized recommendations
- Detect fraud, defects, unusual activity or operational risk
- Extract meaning from text, images, audio and documents
- Support assistants, classification workflows and generative applications
Swiss companies across finance, insurance, manufacturing, healthcare, retail and logistics use these capabilities to improve decisions and automate data-heavy processes. Local teams may need professionals who can collaborate remotely or on site and communicate clearly with business and technical stakeholders.
When freelance expertise helps
Companies bring in freelance specialists when they need a model feasibility assessment, a focused prototype or a production system without expanding their permanent team. Typical deliverables include data preparation, feature pipelines, trained models, evaluation reports, deployment services and monitoring. In Switzerland, external expertise can also help connect distributed teams across German-, French- and Italian-speaking business environments.
What strong professionals deliver
- They establish trustworthy data sources, labels and validation rules
- They compare a sensible baseline with more complex approaches
- They measure precision, recall, calibration, drift and business impact
- They explain model limits, costs, risks and maintenance needs
- They build reproducible pipelines that others can operate
The best Machine Learning specialists balance statistical judgment with software quality. They address privacy, bias, security, explainability and changing data before launch, then document assumptions and create a practical path from experiment to dependable service.
Frequently asked questions
Quick answers to the questions that come up most around Machine Learning.
Machine Learning is used for systems that predict outcomes, recognize patterns or personalize decisions from data. Common applications include recommendation engines, fraud detection, forecasting, computer vision, document processing and intelligent search.
Machine Learning learns relationships from examples, while rule-based software follows logic written directly by specialists. It is useful when patterns are complex or change over time, but rules may be easier to audit when the decision process is simple and stable.
A strong Machine Learning specialist usually combines statistics, Python, SQL and data engineering with software delivery skills. Experience with cloud infrastructure, APIs, MLOps, experiment tracking and responsible data use is valuable when models must run reliably in production.
The right level depends on the risk and scope of the work, not only on the model type. A focused proof of concept needs sound data analysis and evaluation, while a production service requires experience with deployment, monitoring, security, documentation and ongoing model maintenance.
Machine Learning work is often suitable for remote collaboration because data, code and experiments can be shared through secure development environments. On-site workshops may still help with domain discovery, data access and stakeholder alignment, especially when teams work across Swiss language regions.
Assess whether the Machine Learning solution uses appropriate data, a credible baseline and evaluation measures linked to the business decision. Also review reproducibility, error analysis, explainability, operational cost, monitoring and how the system behaves when data changes.
Deep learning can be effective for unstructured inputs such as images, speech and large bodies of text, particularly when sufficient training data and computing resources are available. Classical Machine Learning is often more practical for structured business data, smaller datasets and situations where transparency matters.
Before starting, a Machine Learning specialist should clarify the business objective, available data, ownership, privacy constraints, success criteria and expected users. They should also establish who will operate the model, how predictions enter existing workflows and what happens when confidence is low or the data shifts.
The average hourly rate of freelancers in Switzerland who have used Machine Learning in their recent projects is 121 €, which corresponds to a daily rate of about 964 € based on an 8-hour working day.
Of the freelancers in Switzerland who have used Machine Learning in their recent projects, 94% hold at least a Bachelor's degree, 72% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Switzerland who have used Machine Learning in their recent projects have 24 years of professional experience, with a single engagement typically lasting around 2.5 years.
The most common languages among freelancers in Switzerland who have used Machine Learning in their recent projects are English (100%), German (95%), and French (55%).
The most common industries among freelancers in Switzerland who have used Machine Learning in their recent projects are Information Technology (85%), Banking and Finance (80%), and Education (50%).
The most common business areas among freelancers in Switzerland who have used Machine Learning in their recent projects are Information Technology (90%), Product Development (75%), and Project Management (55%).
Main locations of FRATCH Experts, who have recently used Machine Learning
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
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
