MLOps Experts in Switzerland
matched in minutes with vetted, available freelance specialistsHire experts who productionize machine learning models, automate training and deployment pipelines, and build reliable model monitoring with tools such as Kubernetes, MLflow and cloud platforms. FRATCH connects you with precisely matched, vetted and available freelancers fast.
Meet FRATCH Experts in Switzerland, who have recently used MLOps
Arthur J.
Last position:
Principal AI Consultant & Founder at Modus Data GmbH
Independent Swiss data science and applied AI consultancy: strategy, architecture, and hands-on delivery of custom AI, LLM, and MLOps projects. Available for fractional advisory, architectural audits, and end-to-end project delivery.
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.
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.
Ursula M.
Last position:
Business Mentor at RoleModel Rebels
- Mentor female students and professionals in advancing their careers, particularly as aspiring tech entrepreneurs.
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 MLOps
Aggregated from the professional profiles of matched freelancers.
Experience
23 years
Position duration
2.7 years
Positions per freelancer
10
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Banking and Finance, Information Technology, Education
Certification focus areas
Information Technology, Research and Development, Business Intelligence
Bachelor's degree or higher
100%
Master's degree or higher
83%
Doctorate
17%
Certifications per freelancer
2
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 11 Nov 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Switzerland 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 Switzerland using MLOps
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 11 Nov 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
MLOps 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 (100%)
- Information Technology (100%)
- Education (67%)
- Professional Services (67%)
- Healthcare (50%)
- Government and Administration (50%)
- Telecommunication (50%)
- Aerospace and Defense (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What MLOps Covers
MLOps, short for Machine Learning Operations, applies software delivery and operations practices to machine learning systems. It connects data preparation, experiment tracking, model training, deployment and monitoring so models can run reliably in production. The work spans code, data, infrastructure and governance rather than a single tool.
Production Use Cases
MLOps supports products and internal systems that depend on repeatable model delivery. Typical work includes:
- Creating pipelines for training, validation and release
- Serving models through APIs, batch jobs or streaming systems
- Detecting drift, bias, data quality issues and performance changes
- Reproducing experiments and rolling back model versions
Tools and Ecosystem
Strong MLOps work combines cloud services with open-source tooling. Kubernetes often provides orchestration, while MLflow, Kubeflow or cloud-native machine learning services support experiment tracking and pipelines. Docker, Terraform, Git, CI/CD systems, feature stores and observability tools complete the delivery environment.
When Companies Need Experts
Companies bring in freelance MLOps specialists when prototypes must become dependable products, existing pipelines are difficult to reproduce, or model releases create operational risk. In Switzerland, projects may involve finance, pharmaceuticals, manufacturing, retail or research teams. Remote work is common, while on-site collaboration can help with regulated systems and complex infrastructure.
Skills That Matter
A capable professional understands the full path from data to monitored production service. Look for practical experience with cloud infrastructure, Python, containers, Kubernetes, workflow orchestration, CI/CD, model registries and observability. They should also explain trade-offs around latency, cost, security, reproducibility and model governance in clear terms.
Choosing the Right Specialist
Assess completed systems, not only tool lists. Ask how the specialist handled failed deployments, changing data, model rollback, access controls and unclear ownership between data and platform teams. A strong expert leaves behind documented pipelines, useful alerts, reproducible environments and handover material that another team can operate.
Frequently asked questions
Need clarity? These are the questions we hear most often about MLOps.
MLOps is used to move machine learning models from experiments into dependable production systems. It covers automated training and testing, deployment, monitoring, version control, data quality checks and controlled model updates.
MLOps extends DevOps practices to systems whose behavior depends on data and model versions. It also adds experiment tracking, feature and dataset management, model validation, drift detection and retraining workflows that traditional application delivery does not usually require.
Machine Learning Operations works best alongside cloud infrastructure, Kubernetes, Docker, Python, SQL, CI/CD, Terraform and observability. Familiarity with data pipelines, model serving, security and governance is also valuable because production machine learning crosses several technical boundaries.
MLOps projects need an expert who has operated systems close to the project’s scale, risk and latency requirements. A small batch workflow may need focused pipeline expertise, while a regulated real-time service calls for deeper experience with deployment controls, monitoring, access management and incident response.
MLOps is often suitable for remote collaboration because infrastructure, repositories and cloud environments are accessible online. Swiss projects involving sensitive data, regulated workloads or complex internal networks may still require occasional on-site sessions, and clear communication in the team’s working language matters.
MLOps professionals may work with MLflow, Kubeflow, Kubernetes, Docker, Terraform, GitHub Actions, GitLab CI, Airflow and managed cloud services. The right toolset depends on the existing stack; sound architecture and operational judgment matter more than naming every product.
A strong MLOps specialist can show how models were tested, released, monitored and recovered when conditions changed. Ask for concrete examples of drift handling, reproducible training, rollback design, alert quality, documentation and collaboration with application and data teams.
MLOps deliverables commonly include versioned training pipelines, deployment workflows, model registry configuration, infrastructure definitions, monitoring dashboards and runbooks. A complete handover should make environments reproducible and explain ownership, escalation paths and safe model updates.
The average hourly rate of freelancers in Switzerland who have used MLOps in their recent projects is 112 €, which corresponds to a daily rate of about 896 € based on an 8-hour working day.
Of the freelancers in Switzerland who have used MLOps 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 Switzerland who have used MLOps in their recent projects have 23 years of professional experience, with a single engagement typically lasting around 2.7 years.
The most common languages among freelancers in Switzerland who have used MLOps in their recent projects are German (100%), English (100%), and French (50%).
The most common industries among freelancers in Switzerland who have used MLOps in their recent projects are Banking and Finance (100%), Information Technology (100%), and Education (67%).
The most common business areas among freelancers in Switzerland who have used MLOps in their recent projects are Information Technology (100%), Product Development (100%), and Research and Development (67%).
Main locations of FRATCH Experts, who have recently used MLOps
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:
- Germany
- Switzerland
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Philipp Thomaschewski
FRATCH CEO
