PEFT Experts in Munich
in minutes from over 15,000 CVs with the power of AI.Hire experts who tune large language models with PEFT, LoRA, QLoRA, and adapter methods, then ship low-cost fine-tuning, domain adaptation, and deployment-ready inference. Fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Munich, who have recently used PEFT
Mirza K.
Last position:
Agentic Automation and a RAG system
- This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.
Used: Python, RAG, LangGraph, LangChain, deepeval, MCP
Marco P.
Last position:
Co-founder at Health AI Language Learning Startup
Co-founded an AI-native language learning startup, defining the product vision, AI architecture and technical roadmap. Designed and built the AI and backend stack, including LLM fine-tuning pipelines, custom agentic workflows, and scalable inference infrastructure. First product currently in private beta.
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)
Markus O.
Last position:
Lead E-Solution Architect & Senior Requirements Engineer at Zasterbot-Oracle
- Clarification of project goals, scope, and functional target vision for building the AI-based knowledge base.
- Deriving the initial architecture and implementation strategy for the Zasterbot chatbot, including defining the MVP and expansion phases.
- Developing a functional target vision for building a structured knowledge base and integrating a future chatbot.
- Deriving and prioritizing use cases for information retrieval and provision by the chatbot.
- Modeling data structures and flows for effectively organizing the knowledge base on the Base44 platform.
- Designing and implementing data models for storing and linking relevant information.
- Developing processes for extracting, analyzing, and preparing raw data for the knowledge base.
- Ensuring data consistency and quality as the foundation for the future chatbot.
- Planning the integration of large language models (LLMs) and retrieval-augmented generation (RAG) for precise and context-aware responses.
- Implementing features for analyzing and visualizing data from the knowledge base.
- Using the Base44 platform with JSON-schema-based entities and a flexible permission model.
- Implementing Deno functions for backend logic, event processing, and external API integration.
- Integrating OpenAI services for initial data analysis.
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).
Markus B.
Last position:
Technical Co-Founder at Loka AI
- Software development of a B2B SaaS for AI-based search in internal candidate pools of recruitment agencies
- Design of a multi-tenant, hybrid architecture with dedicated GPU servers and secure cloud integration
- AI-Engineering
- LLMOps
- Python
- FastAPI
Discover over 15,000 top freelancers
Statistics of experts using PEFT
Aggregated from the professional profiles of matched freelancers.
Experience
21 years
Position duration
1.5 years
Positions per freelancer
17
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Education, Automotive
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
67%
Certifications per freelancer
1
Most common languages
German, English, Spanish
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 Munich 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 Munich using PEFT
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.
PEFT 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 (100%)
- Education (83%)
- Automotive (67%)
- Insurance (50%)
- Media and Entertainment (50%)
- Government and Administration (50%)
- Retail (50%)
- Advertising (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
PEFT in practice
PEFT stands for parameter-efficient fine-tuning. It is used to adapt large language models without updating every weight, which keeps training lighter and often faster. Teams use it when they need domain-specific behavior, safer iteration, or lower compute use.
Common methods
PEFT work often centers on methods such as LoRA, QLoRA, adapters, prefix tuning, and prompt tuning.
- Choose the right fine-tuning strategy for the model and task
- Prepare datasets for instruction, classification, or retrieval use
- Keep the base model stable while adding task-specific behavior
- Compare memory use, quality, and deployment constraints
Where it fits
Companies bring in PEFT specialists when a base model is close, but not ready for production. Typical work includes chat assistants, document extraction, internal knowledge search, support automation, and domain-specific classifiers. In Munich, this often appears in enterprise, automotive, industrial, and research settings where model control matters.
What strong experts do
Strong professionals understand model architecture, tokenization, training loops, evaluation, and inference trade-offs. They know when PEFT is better than full fine-tuning and when a prompt-only approach is enough. They also watch for overfitting, data leakage, and unstable outputs.
Tooling and stack
PEFT work usually sits in the Hugging Face ecosystem, with PyTorch, transformers, datasets, accelerate, and model hubs. Many projects also involve vLLM, bitsandbytes, DeepSpeed, or quantization tools for efficient training and serving. Good experts keep the stack practical and easy to reproduce.
Hiring signals
Bring in PEFT expertise when you need to adapt an open model, reduce training cost, or move from prototype to a reliable internal model. It also helps when your team needs help comparing LoRA variants, tuning rank and dropout, or setting up repeatable evaluation. Remote work is common, but on-site collaboration in Munich can help with sensitive data and close product review.
Frequently asked questions
What clients ask us most about PEFT — answered in short.
PEFT is used to adapt large language models for a specific task without retraining the whole model. Companies use it for chat assistants, classification, extraction, retrieval, and domain-specific writing. It is a good fit when the base model already works well and only needs targeted adjustment.
PEFT changes only a small part of the model, while full fine-tuning updates all weights. That usually makes PEFT lighter on memory and easier to iterate. Full fine-tuning can still be useful when the task is far from the base model’s behavior or when maximum control is needed.
PEFT often uses LoRA or QLoRA because they make adaptation practical on limited hardware. LoRA is a common choice for most fine-tuning jobs, while QLoRA adds quantization to reduce memory use further. A good freelancer should explain which option fits the model, the data, and the deployment target.
A strong PEFT specialist should know PyTorch, Hugging Face tooling, dataset preparation, evaluation methods, and basic model serving. It also helps if they understand prompt design, quantization, and retrieval-augmented workflows. The best experts can connect training choices to real product constraints.
PEFT help is useful as soon as you have a clear model use case and a small, usable dataset. You do not need a perfect training pipeline to start, but you do need a defined task and a way to judge outputs. If the target behavior is still vague, a specialist can also help shape the scope.
PEFT projects can be done remotely because most work happens in notebooks, scripts, and review sessions. On-site time in Munich can be helpful when data access is sensitive or when product, legal, and technical teams need close coordination. Many teams use a hybrid setup.
A strong PEFT freelancer can explain why a method was chosen, how training was evaluated, and what trade-offs were accepted. Look for clear experiments, reproducible code, and a practical view of inference cost and latency. Weak candidates talk about fine-tuning in general but cannot defend the model choice.
PEFT changes the model’s behavior through training, while prompt engineering changes it through instructions at inference time. Prompting is faster to test, but it can be brittle for repeated business tasks. PEFT is better when you need steadier output, domain language, or better control over a narrow task.
The average hourly rate of freelancers in Munich, Germany who have used PEFT in their recent projects is 110 €, which corresponds to a daily rate of about 880 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used PEFT in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 67% hold a doctorate.
On average, freelancers in Munich, Germany who have used PEFT in their recent projects have 21 years of professional experience, with a single engagement typically lasting around 1.5 years.
The most common languages among freelancers in Munich, Germany who have used PEFT in their recent projects are German (100%), English (100%), and Spanish (33%).
The most common industries among freelancers in Munich, Germany who have used PEFT in their recent projects are Information Technology (100%), Education (83%), and Automotive (67%).
The most common business areas among freelancers in Munich, Germany who have used PEFT in their recent projects are Information Technology (100%), Product Development (100%), and Business Intelligence (83%).
Main locations of FRATCH Experts, who have recently used PEFT
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:
Berlin Hamburg 
Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
