Support Vector Machine Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Support Vector Machine
Karin Albiez
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 Grunert
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 Parthasarathy
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.
Beshr Alnirabieh
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.
Deepak Reddy Narra
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.
Serge Kalinin
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
Muntaha Shams
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 Suragani
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 Sasi Nair Purayath
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 Kanakagiri
Last position:
Solana Offline Transaction Webapp
- Built a decentralized app using Next.js and Convex DB for secure offline Solana transaction signing.
Muhammad Usman
Last position:
Research Assistant at Saarland University
- Applied AI-driven CADD methodologies for biosynthetic pathway optimization and molecule screening.
- Integrated synthetic biology with computational chemistry workflows for rapid in-silico experimentation.
- Automated ML pipelines using Python, PyTorch, and Scikit-learn on Linux, improving model testing and reproducibility.
Martin Ratajczak
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)
Pawan Saxena
Last position:
CAPTCHA Recognition using CRNN
- Built a CRNN model with VGG16 and BiLSTM backbone for text-based CAPTCHA recognition
- Achieved 9.37% character error rate and 68.36% sequence accuracy on validation data
- Expanded data augmentation pipeline with distortions, noise injection, and clutter to improve robustness
- Conducted detailed error analysis on confusable characters (O, Q, D) and proposed error-specific augmentation
- Tech Stack: Python, TensorFlow/Keras, OpenCV, NumPy, Matplotlib
Sagar Mattikere Anand
Last position:
Graph-Based RAG Agent for Secure Data Intelligence (EcoGraph-RAG) at Philipps University Marburg
- Designed GraphRAG system combining semantic vectors (Chroma) + knowledge graphs (NetworkX/Neo4j) for multi-hop Q&A on climate policy docs.
- Deployed Llama 3/Gemma via Ollama for $0-cost local inference; achieved ~95% entity-relation extraction accuracy.
- Built ingestion pipeline for PDFs + 48k-row CSVs; applied grouped median imputation and fixed data sparsity.
Anton Klonov
Last position:
Head of Technical Overall Integration NSC / Hadoop Cloud Development at IABG
Head of technical overall integration NSC (National Secure Cloud project with about 60 employees).
Technical integration of all subprojects into one product, definition of interfaces, basic components of a cloud including hardware, technical architecture of the IABG base.
Development of a Cloud Management Platform (CMP) that can create a private/mixed cloud of any complexity based on a textual description with one click or interactively.
CMP also includes the complete hardware management cycle.
As a foundation, it uses Kubernetes, OpenStack, and Hadoop.
The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are automatically configured.
The private cloud can run any customer workloads, including a full Hadoop stack with HDFS, Spark, MapReduce, Mesos, HBase and around 20 other ML/DL technologies.
Hadoop worker clusters can also be automatically installed on bare metal or commodity hardware without Kubernetes.
OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.
Development of a Java application Rudi: SOAP, REST, containers, database.
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).
Discover over 15,000 top freelancers
Statistics of experts using Support Vector Machine
Aggregated from the professional profiles of matched freelancers.
Experience
13 years
Position duration
1.7 years
Positions per freelancer
9
Top business areas
Information Technology, Research and Development, Product Development
Top industries
Information Technology, Education, Healthcare
Certification focus areas
Business Intelligence, Information Technology, Research and Development
Bachelor's degree or higher
100%
Master's degree or higher
78%
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 30 Aug 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What SVM does
Support Vector Machine, often written as SVM, is a classic machine learning method for classification and regression. It is used when a team needs a stable model for structured data, text labels, image classes, or anomaly detection. Strong experts know when SVM fits better than deeper models and when it does not.
Typical work
- Binary and multi-class classification
- Margin-based regression with SVR
- Text, spam, and sentiment models
- Outlier and fraud signal detection
- Prototype models for small and medium data sets
Tools and stack
Strong specialists rarely work with SVM alone. They use scikit-learn, NumPy, pandas, and model pipelines for scaling, encoding, feature selection, and validation. In Python projects, they often compare SVM with logistic regression, random forests, or gradient boosting to choose the right fit.
When to bring in help
Companies hire freelance support when an SVM model must be tuned, explained, or brought into production. This is common in analytics teams, product teams, and regulated settings in Germany where clear model behavior matters. It also helps when internal experts need help with kernel choice, parameter search, or feature engineering.
What strong experts do
- Choose kernels and regularization with care
- Build clean preprocessing and scaling steps
- Validate models with the right metrics
- Explain decision boundaries and trade-offs
- Document results for handover and review
Signals of quality
A strong Support Vector Machine specialist can explain why SVM is a good fit, not just how to run it. They test data leakage, class imbalance, and feature scale issues early. They also communicate clearly with data, product, and engineering teams, which is essential for remote work across Germany and beyond.
Frequently asked questions
Before you brief your next project: the most common questions about Support Vector Machine.
Support Vector Machine is used for classification and, in its SVR form, regression. Companies use it for text labeling, document filtering, anomaly detection, and other problems where clear feature separation matters. It is often a good choice when the data set is not huge and the feature space is well prepared.
SVM is often stronger when the boundary between classes is complex and the features are carefully scaled. Logistic regression is simpler and easier to explain, while random forests can handle mixed patterns with less tuning. A good freelancer will compare these options on the actual data instead of assuming SVM is always best.
Bring in a support vector machine specialist when model quality depends on kernel choice, feature scaling, or careful validation. It is also useful when an internal team has a working prototype but needs help turning it into a reliable model. In Germany, this often comes up in industrial analytics, compliance-heavy work, and document processing.
A strong Support Vector Machine expert usually knows Python, scikit-learn, NumPy, pandas, and model evaluation. Feature engineering, data cleaning, and class-imbalance handling matter just as much as the algorithm itself. For production work, knowledge of pipelines, testing, and model handover is also valuable.
A small proof of concept may need only one solid SVM specialist, but production work needs broader ML judgment. The person should know how to tune hyperparameters, avoid leakage, and explain metrics in business terms. If the data is messy or high-stakes, experience with validation and deployment matters more than the model name alone.
Yes, most Support Vector Machine work can be done remotely if the data access, security, and review process are clear. On-site sessions can help when stakeholders need fast workshops on feature design or model interpretation. For many teams in Germany, a remote specialist with strong communication is the practical choice.
A strong SVM freelancer asks about data scale, class balance, feature types, and the business cost of errors before writing code. They should explain why a kernel was chosen, how scaling was handled, and what metrics were used. Good experts also document limits, because SVM can fail quietly when the preprocessing is weak.
Support Vector Machine is usually shortened to SVM, and many searchers use both terms. Some people also look for support vector classifier, support vector regression, or the full phrase support vector machine. A good specialist will know the differences and use the right variant for the task.
The average hourly rate of freelancers in Germany who have used Support Vector Machine in their recent projects is 81 €, which corresponds to a daily rate of about 651 € based on an 8-hour working day.
Of the freelancers in Germany who have used Support Vector Machine in their recent projects, 100% hold at least a Bachelor's degree, 78% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Germany who have used Support Vector Machine in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 1.7 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 (28%).
The most common industries among freelancers in Germany who have used Support Vector Machine in their recent projects are Information Technology (94%), Education (56%), and Healthcare (50%).
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 (94%), 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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Philipp Thomaschewski
FRATCH CEO
