Machine Learning Experts in Zurich
matched in minutes from over 15,000 CVsHire experts who train reliable predictive models, productionize NLP and computer vision workflows, and connect data products to cloud platforms. FRATCH finds vetted, available freelancers whose skills match your project with speed and precision.
Meet FRATCH Experts in Zurich, who have recently used Machine Learning
Marco S.
Last position:
MS Power BI Developer at Bayern Facility Management GmbH
Power BI development and administration, training, consulting, data modeling, DAX development
MS Fabric, MS Power BI, DAX
Christophe L.
Last position:
Independent ML/AI Consultant
AI platforms
- Contributing to the training of all top 5 US large language models through multilingual ML projects, spanning RLHF, evaluations, rubrics, conversations and safety initiatives
- Working across a diverse range of training methodologies, supporting model alignment and quality improvement in multiple languages
Patrick B.
Last position:
Senior Software Engineer at On AG
- Since August 2025 I'm working on the Conversational AI team. We are developing an internal platform for the future of Conversational Ecommerce, powering other teams to use the Agents in their user experience. I'm deeply focused on the architecture of this project, with a particular focus on the monitoring and live evaluation of the agents.
- Played a pivotal role in business-critical operations, including Adyen client upgrades, multi-account configuration for app releases, domain migration support, and implementing a crucial BFCM hotfix to resolve a 10-minute update query (on the checkout workflow) that was blocking sales.
- Served as a key member of the On-Call rotation for over a year, leading >15 incidents, contributing to system stability, and enhancing distributed tracing via New Relic integration, deepening system-wide understanding and operational excellence.
- Conceived and delivered an MVP for a "Low Stock in Key Variants" (LSKV) index within two weeks as part of the Conversion Rate Taskforce, using statistical analysis of size curves to identify OOS products; this initiative led to a (0.9%) CVR improvement post-handover.
- Led major performance and stability initiatives, including a Ruby upgrade (YJIT, Jmalloc) that slashed average response times from 220ms to <100ms, and initiated an "Order Table Cleanup" project to manage 10M+ order records, ensuring SOX compliance and database health.
- Architected and delivered a scalable CloudFlare Worker solution for critical Apple Pay domain validation, mitigating operational risks and establishing a best-practice, automated workflow that was subsequently leveraged by multiple teams, demonstrating cross-functional impact
- Drove substantial evolution of the core e-commerce backend through consistent, high-impact contributions (130+ merged PRs), spanning critical feature delivery, complex system optimizations, and enhancing platform stability."
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.
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
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.
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.
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
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
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.
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
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
2.6 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
100%
Master's degree or higher
80%
Doctorate
13%
Certifications per freelancer
4
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 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 29 Oct 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 (88%)
- Banking and Finance (81%)
- Education (63%)
- Professional Services (44%)
- Telecommunication (38%)
- Healthcare (31%)
- Government and Administration (31%)
- Retail (31%)
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 use them for predictions, recommendations, classifications or automated decisions. It supports products such as fraud detection, demand forecasting, search, personalization, document processing and industrial quality control. The work depends on sound data, clear objectives and measurable outcomes.
Models and methods
Professionals select methods that fit the problem rather than forcing every use case into deep learning. Their toolkit may include regression, decision trees, gradient boosting, clustering, recommendation systems, neural networks and large language model workflows. They also design features, labels, validation strategies and safeguards against leakage or bias.
Ecosystem and tooling
A production project often spans Python, SQL and notebooks alongside libraries such as scikit-learn, PyTorch, TensorFlow or XGBoost. Specialists may use MLflow, Kubeflow, Airflow, Docker and cloud services for experiment tracking, orchestration and deployment. Strong data pipelines, version control, testing and observability keep models maintainable after launch.
Where companies use it
Machine Learning appears across finance, healthcare, mobility, manufacturing, retail, media and software products. Typical deliverables include:
- Forecasting and anomaly detection systems
- Recommendation, ranking and personalization models
- NLP, document intelligence and conversational features
- Computer vision for inspection or image understanding
- Model APIs, batch pipelines and monitoring dashboards
When external expertise helps
Companies bring in freelance specialists when internal teams need to turn an uncertain idea into a tested model, modernize an existing pipeline or prepare a system for production. Support is also valuable when data quality is unclear, model performance has stalled or business teams need transparent explanations. In Zurich, remote collaboration can work well, while on-site workshops may help with regulated, industrial or multilingual use cases.
What strong specialists deliver
The best professionals connect model quality with business value. They clarify the decision the model should support, establish a credible baseline and explain trade-offs in language stakeholders can use. They document data lineage, test edge cases, plan retraining and monitor drift, latency, cost and fairness. They also know when a simpler rule, analytics workflow or human review is safer than automation.
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, recommendations, classification and automation. Common projects include fraud detection, forecasting, search ranking, customer segmentation, document extraction and visual inspection.
Machine Learning learns patterns from examples, while traditional software usually follows rules written directly by people. Statistics supplies many of its foundations, but ML projects often focus more on predictive performance, automation and handling complex or high-dimensional data.
A strong Machine Learning specialist usually combines Python, SQL, data preparation and experiment design with software engineering practices. Experience with cloud infrastructure, APIs, MLOps, data visualization, privacy, model explainability or a domain such as finance can be important depending on the project.
The right level of Machine Learning experience depends on the deliverable, data maturity and risk of the use case. A proof of concept may need focused modeling and analysis, while a production system requires expertise in deployment, monitoring, security, testing and retraining.
Machine Learning work is often well suited to remote collaboration because data exploration, coding and model reviews happen in shared environments. Zurich teams may still prefer on-site sessions for access-controlled data, stakeholder workshops or close coordination with manufacturing and regulated operations.
Look for a Machine Learning professional who can explain the baseline, data limitations, validation method and business impact without hiding behind a score. Ask how they handle leakage, bias, drift, reproducibility, monitoring and failure cases, then review evidence of production ownership rather than only prototype work.
Machine Learning is a major part of artificial intelligence, but AI also includes rule-based reasoning and other approaches. Deep learning is a subfield of ML based on multi-layer neural networks, and it is especially useful for language, images, audio and other complex data when suitable training data and infrastructure are available.
A practical Machine Learning project should deliver a validated model, documented data pipeline, reproducible training process and a clear serving or batch setup. It should also define ownership, monitoring, retraining triggers, security controls and a fallback path when predictions are uncertain or data changes.
The average hourly rate of freelancers in Zurich, Switzerland who have used Machine Learning in their recent projects is 110 €, which corresponds to a daily rate of about 882 € 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, 80% hold at least a Master's degree, and 13% 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 2.6 years.
The most common languages among freelancers in Zurich, Switzerland who have used Machine Learning in their recent projects are English (100%), German (88%), and French (56%).
The most common industries among freelancers in Zurich, Switzerland who have used Machine Learning in their recent projects are Information Technology (88%), Banking and Finance (81%), and Education (63%).
The most common business areas among freelancers in Zurich, Switzerland who have used Machine Learning in their recent projects are Information Technology (94%), Product Development (75%), and Project Management (56%).
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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