Support Vector Machine Experts in Germany
for precise predictive models, matched fast with vetted freelancersHire experts who build classification, regression and anomaly-detection models with Support Vector Machine, scikit-learn and Python, then validate them against real business data. FRATCH matches you quickly and precisely with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used Support Vector Machine
Gabin Maxime N.
Last position:
Multi-Agent R&D Pipeline (3 Custom Agents) at Independent Project
Claude Code subagents, MCP, Pydantic V2, pytest, bandit
Designed and shipped 3 specialized agents that hand work down a line: a research agent writes a cited implementation spec, a coding agent builds the modular code and its tests, a review agent ranks findings by severity and applies the fixes. Each handoff is a structured document, so no stage depends on another agent's context window.
Connected the research agent to an academic-research MCP server (Semantic Scholar, ArXiv, Hugging Face Hub, citation snowballing) so every reference traces to a tool result rather than the model. Gated commits behind ruff, mypy, pytest and bandit, required human sign-off before installs and commits, and persisted session state on disk so long runs survive a context reset.
Karin A.
Last position:
AI Benchmark Engineer | Native language specialist German at Lilt
- Task Engineering: Evaluating Coding Agents.
- Asset Creation: Building realistic task environments using datasets and files in German. Crucially, these assets must remain in the target language to genuinely measure multilingual handling.
- Prompting & Translation: finding failure points where AI does not work, in German.
- Implementation & Verification: Supporting the development of robust solutions (reference implementations) and write highly reliable, deterministic verifier scripts (using rubric-based judging only when strictly necessary).
- Calibration & Execution: Analyze execution logs and calibrate task difficulty (Easy to Very Hard) using standard Terminal-Bench run configurations against various model tiers (Haiku, Opus).
- Quality Assurance: Participation in a rigorous, 4-layer human quality control process (creation, human review, calibration review, and audit) alongside automated LLM-based checks to ensure fairness, grammatical accuracy, and benchmark integrity.
- Linguistic Review: Reviewing AI benchmark tasks across Hindi, Arabic, Japanese, Chinese, Czech and Turkish.
Philipp G.
Last position:
Data Scientist & ML Engineer at Data-Science Factory GmbH
- Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
- Implementation of automated end-to-end cloud processes
- Development of LLM and NLP models
- Creation of interactive reports
- Support for national and international large corporations as well as medium-sized companies in implementing ML projects
Ashwin P.
Last position:
Freelance Data Scientist at Mercor Intelligence
- Architected and deployed end-to-end machine learning pipelines across classification and prediction datasets, ensuring robustness and reproducibility through MLOps best practices.
- Contributed directly to LLM model output accuracy improvement by designing and engineering specialised prompts grounded in end-to-end ML and SciML pipeline logic.
- Developed training data for large language models by formulating coding problems that models could not resolve and subsequently documenting the correct solutions.
Manoj K.
Last position:
Data Analyst Work Student at Biebelhausener Mühle seit 1647 GmbH
- Managed and maintained daily sales and transaction data, ensuring data accuracy and integrity for operational reporting and analysis.
- Analyzed customer purchasing patterns to support inventory planning and improve product availability.
Beshr A.
Last position:
System Administrator – HealthCare IT & Data Infrastructure at Cellitinnen Hospital Association
- Integration of medical modalities (including ultrasound) into the existing IT infrastructure (DICOM, HL7) – put into operation within the planned timeframe.
- Administration and optimization of PACS systems for efficient archiving and distribution of radiology image data across multiple locations.
- Ensuring consistent data quality and seamless interoperability in data exchange between HIS, RIS, and PACS.
- Close collaboration with medical staff to analyze and digitally optimize clinical workflows.
- Requirements management and test coordination when implementing clinical requirements in complex IT structures.
Serge K.
Last position:
MLOps (machine learning operations) at REWE Digital GmbH
- It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
- GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
- Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
- CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment
Deepak R.
Last position:
Machine Learning Engineer at go AVA GmbH
- Designed and built a multi-tenant Python/Flask API platform with JWT + API-key authentication, scoped access control, and service-level orchestration as the backbone for AI applications.
- Built a multimodal RAG system with hybrid chunking, dense/sparse embeddings, hybrid retrieval, reranking, and vector search to deliver grounded, high-precision responses across enterprise data.
- Productionized AI workflows with Docker, CI/CD, Redis-backed async job tracking, webhook callbacks, external AI/media service integrations, and runtime health/reliability controls.
Martin R.
Last position:
Senior LLM Research Scientist at BYO Inc.
- Research and develop models for chatbots, NLP and LLMs (e.g. Llama, Qwen, OpenAI)
- Enhance chatbots with RAG, in-context learning
- Supervised fine-tuning (PEFT, LoRA), Huggingface or Unsloth
- Advanced training methods: Test-time training, (transductive) active learning, reinforcement learning
- High-throughput serving with vLLM
- Apply embedding models (e.g. SentenceTransformers), similarity/vector search or vector DB or ranking (e.g. LlamaIndex, Faiss, LangChain)
- Generate and filter synthetic data, clustering
- Detect hallucinations
- Evaluate chatbot models (Rouge, BLEU, F1-Score, Recall, Precision)
- Visualization of experiments (matplotlib)
Anton K.
Last position:
Head of Overall Technical Integration NSC / Hadoop Cloud Development at IABG
Head of overall technical integration NSC (National Secure Cloud, project with approx. 60 employees).
Technical integration of all subprojects into one product, definition of interfaces and basic components of a cloud including hardware, technical architecture of the IABG platform.
Development of a Cloud Management Platform (CMP) capable of creating private/mixed clouds of any complexity based on a textual description with one click or interactively.
CMP also includes the complete hardware management lifecycle.
Kubernetes, OpenStack and Hadoop are used as the foundation.
The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are configured automatically.
Private cloud can run any customer workloads, including a full Hadoop layer with HDFS, Spark, MapReduce, Mesos, HBase and around 20 additional ML/DL technologies.
Hadoop worker clusters can also be installed automatically without Kubernetes on bare metal or commodity hardware.
OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.
Development of a Java application Rudi: SOAP, REST, containers, DB.
Technologies: Kubernetes (K3s, Rke2, Minikube, Harbor, Gitea, Jenkins, Longhorn, Keycloak, Rancher), OpenStack (Nova, Neutron, Keystone, Swift, Ceph, Cinder, Sahara, Magnum, Kayobe, Kolla, Bigrost, Ironic), Hadoop (HDFS, Ambari, Solr, Livy, Ranger, YARN, Tez, HBase, Kafka, Hive, Zookeeper, MapReduce, Spark, Oozie, Flink), virtualization (Kubernetes (K3S), VMware, Oracle), scripting (Ansible, Puppet, Juju, Shell, Groovy, Gradle, Maven).
Muntaha S.
Last position:
AI Engineer (Freelance) at Upwork
- Delivered 40+ AI projects and 23 strategic consultations for international clients (US, Europe, Middle East), achieving a 98% job success rate and building long-term partnerships.
- Developed and deployed production-grade AI solutions in computer vision, NLP, deep learning, and generative AI (LLMs, RAG pipelines, Stable Diffusion, OCR, chatbots), enabling automation and improving client efficiency by up to 70%.
- Designed and fine-tuned large language models (LLMs), including prompt engineering and integration with enterprise knowledge bases, leading to smarter decision-making and reduced manual effort.
- Built real-time computer vision applications (detection, segmentation, OCR) and integrated them into business systems, significantly enhancing accuracy and scalability.
- Consulted startups and enterprises on AI strategy, architecture, and deployment (cloud & on-premise), accelerating product development and reducing time-to-market.
- Managed complete AI project lifecycles (requirements gathering, solution design, deployment, support) in agile, international, and cross-functional environments, ensuring high-quality delivery.
Reshmi S.
Last position:
Software Engineer at Aumovio Engineering Services (formerly Continental Engineering Services)
- Programming: C/C++, Python, Embedded C, MATLAB
- Feature Owner for SecOC and FvM, leading development, integration, and validation
- Strong ECU hardware understanding for debugging
- Integrated AUTOSAR security modules: CSM, Crypto, CryIf, and HSM
- Hands-on experience with AUTOSAR BSW and MCAL configuration
- Implemented Secure Boot with DMA on Chorus MCU, improving boot performance
- Designed HSM key management and UDS-based key verification features
- Developed Python automation scripts to improve validation efficiency
- Performed ISO 26262 and ASPICE compliant development and testing
- Implemented diagnostics (DIDs, DTCs) for fault detection and reliability
- Strong knowledge of 32-bit MCU architectures and real-time systems
- Proficient in embedded C, compiler/debugger tools, and CANoe
- Experience with TLS, IPsec, key management, and Ethernet switch configuration
- Created architecture and system documentation for cross-team alignment
- Supported production ECU flashing and large-scale deployments
- Conducted functional safety-related tests to ensure system reliability
Aravind S.
Last position:
AI – Data Specialist at Emirates Islamic Bank
- Architected and deployed LLM based AI agents, RAG pipelines, and vector search solutions for decision support across retail banking department.
- Developed and shipped robust AI pipelines with guardrails, error handling, monitoring, and fallback logic ensuring high reliability outcomes and compliance with data privacy.
- Developed and deployed ML models to identify transactional anomalies, improving fraud detection and risk assessment in high-volume datasets for credit risk modelling.
- Built, evaluated and fine-tuned ML models to generate propensity scores for customers used to drive personalized targeting campaigns for credit cards and personal finance/loan products.
- Developed an NLP pipeline using BERT embeddings and spaCy NER for SMS/email analysis and customer query logs.
- Trained machine learning models using Isolation Forest to classify user behaviour and detect anomalies.
- Extracted, cleaned, enriched and feature engineered datasets from different sources to build feature stores that powered ML model training.
- Led development of dashboards using Power BI, Grafana, and Prometheus to monitor model performances, KPI trends, and marketing metrics.
- Built multi-touch attribution models using logistic regression and time-decay weights to evaluate lead quality.
- Developed scalable ETL pipelines from CRM, T24, SAP, and ERP, supporting millions of monthly transactions.
- Integrated testing and CI/CD workflows for robust data pipeline deployment.
Abhishek K.
Last position:
Solana Offline Transaction Webapp
- Built a decentralized app using Next.js and Convex DB for secure offline Solana transaction signing.
Martin M.
Last position:
Freelance Data Architect at Zeppelin
- Evaluation and scoring of various technologies as future telematics platform (Kafka Streams, Spark, Splunk, Snowflake)
- Improve test framework and scalability of Telematics streaming service (Scala, Property-Based Testing, Kafka, Kafka Streams, Kubernetes)
Discover over 15,000 top freelancers
Statistics of experts using Support Vector Machine
Aggregated from the professional profiles of matched freelancers.
Experience
14 years
Position duration
1.8 years
Positions per freelancer
9
Top business areas
Information Technology, Research and Development, Product Development
Top industries
Information Technology, Education, Healthcare
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
96%
Master's degree or higher
78%
Doctorate
26%
Certifications per freelancer
2
Most common languages
German, English, 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 Germany 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 Germany using Support Vector Machine
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.
Support Vector Machine 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 (96%)
- Education (57%)
- Healthcare (48%)
- Automotive (43%)
- Banking and Finance (35%)
- Manufacturing (35%)
- Professional Services (30%)
- Aerospace and Defense (17%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Support Vector Machine does
Support Vector Machine, commonly called SVM, is a supervised learning method for classification, regression and novelty detection. It separates observations by finding a decision boundary with the strongest possible margin. With kernel functions, it can also model complex relationships without explicitly transforming every input feature.
Models and applications
SVM is useful when labelled datasets are limited, features are numerous, or decision boundaries need to be controlled carefully. Typical work includes:
- Text classification for search, routing and document review
- Image and signal recognition with engineered features
- Fraud, quality and fault detection
- Regression for forecasting continuous business values
- Novelty detection for unusual operational behaviour
Ecosystem and tooling
Most projects use Python with scikit-learn, NumPy, pandas and established evaluation tools. Specialists may also work with LIBSVM, Spark MLlib or PyTorch when SVM forms part of a broader machine learning workflow. Effective pipelines cover feature scaling, encoding, cross-validation, hyperparameter search and model persistence.
When to bring in expertise
Companies often need freelance support when a proof of concept must become a tested service, when an existing model produces unstable results, or when domain data needs careful preparation. In Germany, SVM solutions can support manufacturing, finance, healthcare, mobility and industrial monitoring, provided data access and governance are handled from the start.
- Define the target, labels and evaluation method
- Prepare reproducible training and inference pipelines
- Compare kernels, regularisation and alternative models
- Document limitations and hand over maintainable workflows
What strong specialists contribute
A strong professional understands both the mathematics and the operating context. They choose a suitable kernel, scale features correctly, address class imbalance and explain precision, recall, margins and support vectors in business terms. They also test leakage, drift and inference performance instead of relying on a single score.
Collaboration and delivery
SVM projects can be delivered remotely when data access, environments and review routines are well defined. On-site collaboration may help where models depend on production equipment, laboratory processes or sensitive internal workflows. German and English communication can both matter, especially when specialists must align data teams, subject experts and stakeholders around assumptions and model limits.
Frequently asked questions
Before you brief your next project: the most common questions about Support Vector Machine.
Support Vector Machine is used for supervised classification, regression and novelty detection. Companies apply it to text categorisation, image recognition, quality control, fraud analysis and other problems where labelled data and well-defined features are available.
SVM can perform strongly on smaller or medium-sized, high-dimensional datasets and offers useful control through margins and kernels. Decision trees are often easier to explain, while neural networks may be more suitable for very large datasets or raw images, audio and language.
A capable Support Vector Machine specialist should also understand Python, scikit-learn, pandas, NumPy, data cleaning and model evaluation. Experience with feature engineering, SQL, experiment tracking and deployment helps turn a model into a dependable workflow.
The right level depends on the task rather than a fixed duration. A simple benchmark may need focused modelling skills, while a production system requires experience with data quality, validation, monitoring, reproducibility and integration with existing services.
Yes, many Support Vector Machine assignments work remotely when secure data access, documentation and regular reviews are available. On-site work can be useful for projects tied to factory equipment, laboratory measurements or restricted operational processes.
Ask how the specialist would define the target, prevent data leakage and select an evaluation method. Strong candidates explain scaling, kernel choice, class imbalance and error trade-offs clearly, then show how they would make results reproducible and useful in production.
Support Vector Machine can handle imbalanced data when class weights, suitable metrics and sampling strategies are chosen carefully. Noise still requires investigation, because outliers and incorrect labels can shift the boundary and reduce generalisation.
A complete SVM engagement should deliver prepared data workflows, a trained and validated model, documented parameters and reproducible evaluation. Depending on the scope, it may also include a prediction service, integration guidance, monitoring needs and a clear handover.
The average hourly rate of freelancers in Germany who have used Support Vector Machine in their recent projects is 83 €, which corresponds to a daily rate of about 664 € based on an 8-hour working day.
Of the freelancers in Germany who have used Support Vector Machine in their recent projects, 96% hold at least a Bachelor's degree, 78% hold at least a Master's degree, and 26% hold a doctorate.
On average, freelancers in Germany who have used Support Vector Machine in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Germany who have used Support Vector Machine in their recent projects are German (100%), English (100%), and French (35%).
The most common industries among freelancers in Germany who have used Support Vector Machine in their recent projects are Information Technology (96%), Education (57%), and Healthcare (48%).
The most common business areas among freelancers in Germany who have used Support Vector Machine in their recent projects are Information Technology (100%), Research and Development (91%), and Product Development (83%).
Main locations of FRATCH Experts, who have recently used Support Vector Machine
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.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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FRATCH CEO
