Machine Learning Experts in Zurich
matched in minutes from over 15,000 CVsHire experts who design predictive models, production-ready ML pipelines and computer vision or natural language solutions. FRATCH precisely matches you with vetted, available freelancers who fit your technical needs and project context.
Meet FRATCH Experts in Zurich, who have recently used Machine Learning
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.
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.
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.
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
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
Lars N.
Last position:
Coordinator of a scientific OpenSource project
- Coordination of a scientific OpenSource project
- Administration of publication in the IEEE proceedings
- Presentation at the most prominent international conference for neural networks (WCCI IJCNN 2022)
Discover over 15,000 top freelancers
Statistics of experts using Machine Learning
Aggregated from the professional profiles of matched freelancers.
Experience
23 years
Position duration
2.8 years
Positions per freelancer
8
Top business areas
Information Technology, Product Development, Project Management
Top industries
Banking and Finance, Information Technology, Education
Certification focus areas
Information Technology, Project Management, Marketing
Bachelor's degree or higher
100%
Master's degree or higher
83%
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 Zurich 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 Zurich 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.
- Banking and Finance (85%)
- Information Technology (85%)
- Education (62%)
- Professional Services (46%)
- Telecommunication (38%)
- Media and Entertainment (31%)
- Government and Administration (31%)
- Healthcare (23%)
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 or recommendations without explicitly coded rules for every case. Teams use it for fraud detection, demand forecasting, search, personalization, document processing and industrial quality control. The work spans experimentation, model development and reliable operation in production.
Models and methods
Professionals choose methods based on the data, decision and operational constraints. Common approaches include supervised and unsupervised learning, time-series forecasting, recommendation systems and deep learning with neural networks. They also handle feature engineering, evaluation, explainability and the balance between accuracy, latency and maintainability.
Ecosystem and tooling
The ML ecosystem connects data work with software delivery and cloud infrastructure. Specialists may use Python, scikit-learn, TensorFlow or PyTorch alongside pandas, SQL, notebooks and experiment tracking tools. Production projects often involve Docker, Kubernetes, model registries, feature stores, APIs and monitoring for drift, data quality and changing model performance.
Where companies use it
Machine Learning appears in products and internal systems across finance, healthcare, retail, mobility, manufacturing and media. Typical deliverables include:
- Forecasting and anomaly detection systems
- Recommendation and ranking models
- Computer vision for inspection or document extraction
- Natural language classification, search and summarization
- Scalable inference services and monitoring
When freelance expertise helps
Companies often bring in specialists when they have valuable data but lack a clear path from prototype to measurable business value. External expertise can help assess data readiness, define a target metric, select a suitable model and create a maintainable deployment process. In Zurich, remote collaboration is common, while regulated or hardware-related work may benefit from planned on-site sessions and clear communication in English or German.
What strong specialists bring
Strong professionals connect statistical reasoning with practical software delivery. They ask how labels were created, test for leakage and bias, establish meaningful baselines and communicate uncertainty instead of hiding it. They also document assumptions, build reproducible pipelines and work with product, data and infrastructure teams so the model remains useful after launch.
Frequently asked questions
Key details about Machine Learning, drawn from the questions we get asked most.
Machine Learning is used to identify patterns in data and support predictions, classifications, recommendations and automated decisions. Common applications include fraud detection, forecasting, search ranking, document understanding, customer personalization and visual inspection.
Machine Learning learns relationships from examples, while rule-based software relies on logic written directly by specialists. ML is useful when patterns are complex or change over time, but rules may be easier to audit when decisions are simple, stable and strictly defined.
Deep learning is a branch of Machine Learning that uses multilayer neural networks. It can perform well with images, audio, language and other high-dimensional data, while conventional ML methods may be more suitable when datasets are smaller or interpretability is important.
A strong Machine Learning specialist usually combines statistics, Python, SQL and data preparation with software engineering practices. Experience with cloud services, APIs, Docker, model monitoring, data pipelines and responsible AI can be important for production work.
The right level depends on the work rather than a fixed duration. A discovery project may need a specialist who can assess data and define a baseline, while a production system requires proven experience with deployment, monitoring, retraining and integration into existing software.
Machine Learning work is often well suited to remote collaboration because data exploration, model training and code reviews happen online. On-site sessions can still help when teams handle sensitive data, physical equipment or complex stakeholder decisions in Zurich; agree on access, language and meeting routines early.
Ask the Machine Learning professional to explain the business objective, data limitations, baseline and evaluation method in clear terms. Good specialists discuss leakage, bias, uncertainty, reproducibility and operational monitoring, not just an impressive model score.
Predictive modeling may be the better choice when structured data, clear targets and explainable outputs matter. More complex approaches such as deep learning or generative AI are justified when the data type and product need support them, not simply because they are newer.
The average hourly rate of freelancers in Zurich, Switzerland who have used Machine Learning in their recent projects is 119 €, which corresponds to a daily rate of about 956 € based on an 8-hour working day.
Of the freelancers in Zurich, Switzerland who have used Machine Learning in their recent projects, 100% hold at least a Bachelor's degree, 83% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Zurich, Switzerland who have used Machine Learning in their recent projects have 23 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 Machine Learning in their recent projects are English (100%), German (92%), and French (54%).
The most common industries among freelancers in Zurich, Switzerland who have used Machine Learning in their recent projects are Banking and Finance (85%), Information Technology (85%), and Education (62%).
The most common business areas among freelancers in Zurich, Switzerland who have used Machine Learning in their recent projects are Information Technology (92%), Product Development (77%), and Project Management (54%).
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.
Countries:
Zurich 
Geneva Basel Bern Request a free demo
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