Fine-Tuning Experts in Munich
in minutes from over 15,000 CVs with the power of AI.Hire experts who adapt large language models, improve domain-specific outputs, and tune prompts, datasets, and evaluation flows for real production use. Get fast, precise matching with vetted, available freelancers in Munich.
Meet FRATCH Experts in Munich, who have recently used Fine-Tuning
Tezcan Dilshener
Last position:
Solution Architect / Project Manager at German Football Association
- Overall responsibility for the project lifecycle from scope definition to completion
- Close collaboration with platform teams, IT leaders, and external service providers
- Application of SAFe principles and structured sprint work
- Creation of a migration roadmap with clear milestones
- Monitoring of the lifecycle: onboarding, repository migration, replication of permissions, and system tests
- Visualization of the architecture with PlantUML and Gliffy as well as documentation in Confluence
- Regular status reports and running knowledge transfer sessions
Thomas Langer
Last position:
Consultant for AI-driven process automation at Lumiz
AI-driven automation of purchasing on a printing company's website, including selecting delivery times, order options, ordering, payment, and uploading print data from the Lumiz Cloud.
Konstantinos Metaxas
Last position:
IT-Fly Specialist – Global Rollout at Lufthansa Group
Plan & Prepare (Site Design & Readiness): Inventory & Design: Dell PowerEdge R-Series, Aruba switches (L2/L3), notebooks/peripherals; serials/asset tags, IPv4/IPv6 addressing, VLAN-/DHCP-/DNS plan
Runbooks/MOPs: site rollout runbook, backout strategy (<15–30 min), risk register, communication matrix; approvals via CAB/change
Images/Packages: Golden Image (Win10/11), driver packs, BIOS/UEFI baseline; O365/Teams/OneDrive KFM; BitLocker policies; MECM/SCCM, Intune/Autopilot, MDT/WinPE
Logistics: shipping/customs clearance, RMA/DOA, on-site spares; tools (barcode scanner, label printer), "Go-Bag" (cables, SFPs, console cables)
Deliver (on-site implementation): End devices: swap & migration (USMT/OneDrive KFM), peripherals (ATB/BT printers, scanners, boarding gate hardware); domain join, compliance checks, O365 activation, printers/queues, network drives
Acceptance: functional tests for DCS/CUTE/CUPPS/CUSS stations, ticketing/check-in workflows, boarding gates
Server (R-Series): rack & stack, cabling (PDU redundancy, fiber/copper), labeling/naming; firmware/RAID (PERC), Lifecycle Controller, iDRAC network; Windows Server 2022/2025 + CIS/BSI hardening; agents (backup/AV/EDR/monitoring), time service/NTP auth, Syslog/SNMPv3
Network (Aruba): VLANs, LACP trunks, MSTP root; PortFast + BPDU Guard at the edge; QoS (EF/AF); dual stack (v4/v6), DHCP relay. NAC/802.1X with ClearPass/Radius/TACACS+, roles + MAB fallback; guest isolation, ACLs (Guest→Mgmt deny). Telemetry: sFlow, SNMPv3, Syslog→SIEM; LLDP→inventory/CMDB
Airport specifics: CUTE/CUPPS/CUSS terminals; DCS/Amadeus/SITA connectivity; FIDS (read-only); bag tag/boarding pass printing; changes in off-peak/night windows
Stabilize (hypercare): first-day support, KPI tracking (login times, ticket volume, error classes), QoS fine-tuning
Troubleshooting: Wireshark/iperf, event logs, switch counters, sFlow flows; fast incident handling as SPOC
Knowledge transfer: short training sessions for station teams, mini-runbooks (fault/recovery)
Close (documentation & handover): docs & CMDB: final configs (switch/server), topology/patch plans, IP tables, serial/asset lists, before/after photos
Acceptance & sign-off: UAT protocols, functional evidence (use cases), return/reuse of old hardware
Lessons learned: risks, standard packages, driver freeze, "known issues"
Interfaces/communication: station IT, airport IT, SOC/NOC, ground ops/ramp/check-in, provider (SITA/Amadeus). ITSM: ServiceNow (Inc/Req/Change/KB), handover to BAU
Markus Oberhammer
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.
Wolfgang Horlacher
Last position:
Head of Finance and Controlling (Interim) at Kunert Fashion Group
Financial accounting: daily general ledger entries and month-end closing
Preparation of FY 2024 financial group statements (subunits KF, KNL, KAU, KCH)
Preparation of physical inventory for KF with inventory instructions for Logistic
Further development of ERP Finance processes integration with MS Dynamics BC 14
Implementation of electronic invoice verification and Multi Cash banking software
Expansion of sales and e-commerce controlling toolbox and processes
Preparation of budgets 2026, 2027, 2028, including investment plan, cost accounting
Preparation and commentary on monthly reports for sub-units and Group
Preparation of weekly financial plans, cash flow and liquidity plans
Further development of sales controlling, focusing on margins & product profitability
Creation and integration of accounting workflows for MS Dynamics
Leadership focus: Stabilization of the finance team and clear scope of work responsibility
Communication with banks, auditors, tax advisors, and tax authorities
Complete and accurate monthly reporting for each business unit and the KF Group
Preparation 2024 financial statements for KF Germany, KNL, KAU, and KNL with audit opinion
Detailed setup and preparation of the KF 2026 budget for the management and shareholders
Weekly cash flow planning coordinated with the management and shareholders
Turnaround in net sales and EBIT from September 2025 onwards – stable growth
Leadership & change through consistent, data-driven KPIs
Raghu Ram Vadali
Last position:
Telco Customer Churn Prediction – End-to-End ML Pipeline at Self-Initiated Project
- Designed and implemented a full machine learning pipeline for churn prediction using the Telco dataset.
- Applied preprocessing techniques including missing value handling, categorical encoding, feature scaling, and PCA.
- Built and compared over 15 models (logistic regression, random forest, XGBoost, etc.) and evaluated them using accuracy, precision, recall, F1 score, ROC AUC, and PR AUC.
- Tuned hyperparameters with GridSearchCV, achieving 80.6% accuracy with random forest and XGBoost.
- Created visual reports (bar plots, heatmaps, radar charts) to interpret model performance and churn drivers.
- Exported reusable pipelines and trained models with joblib for deployment.
Nima Nooshi
Last position:
Co founding LLM Engineer at LLM Ventures
- Co-founded an AI venture focused on building production-grade LLM applications and agentic systems
- Designed and implemented multi-agent AI workflows for financial and trading applications
- Developed LLM-powered copilot architectures for portfolio analysis, trade management, and personalized user coaching
- Built on-device and edge-deployed inference applications, optimizing models for low latency, privacy, and resource-constrained environments
- Led system architecture decisions across model selection, orchestration, state management, and deployment
Stephan Baier
Last position:
Freelance Data Scientist at Baier Data & AI Consulting
David Thompson-Ajayi
Last position:
AI Trainer (NLP & LLM Evaluation) at Freelance
- Designed and evaluated high-quality prompts and completions for Large Language Models (LLMs), focusing on improving response accuracy, instruction-following behavior, and factual consistency.
- Annotated and rated LLM-generated outputs for grammar, coherence, relevance, and truthfulness.
- Developed RLHF-style preference data by ranking model completions to inform reinforcement learning fine-tuning cycles.
- Participated in prompt engineering experiments to assess the effect of instruction format, verbosity, and phrasing on model behavior.
- Conducted error analysis and quality assurance on large-scale NLP datasets, identifying edge cases and linguistic ambiguity affecting LLM performance.
Marco Pennacchiotti
Last position:
Head of Data Science and Data Engineering at Entrix
- Established and leading multi-year research roadmap
- Developed and implementing hiring plan for science and data
- Spearheading data engineering efforts in the company
- Led the team to deploy a new trading algorithm, increasing assets’ revenue of 18%
Sebastian Lingenfelter
Last position:
LLM Evaluation Response Specialist at Translated.com
- Created and refined technical and compliance-oriented datasets for AI, ensuring high-quality structured documentation.
- Conducted supervised fine-tuning (SFT) and RLHF tasks, maintaining strict alignment with industry and security guidelines.
- Produced detailed technical reports and feedback for audits and QA teams.
- Collaborated with cross-functional teams on documentation strategies for large-scale AI deployments.
Vasco Almeida
Last position:
AI Research Intern – Generative AI at BMW AG
- Designed and implemented multi-modal entertainment toolchains that combine passenger input, vehicle context, large-language models (text-to-text and speech-to-speech) and image generation models to deliver more interactive and immersive in-car experiences.
- Built and orchestrated tools for LLM-based agents, covering session management, background task execution, dynamic user interactions and persistent application state.
- Investigated multi-agent orchestration frameworks for in-car environments, evaluating communication protocols and architectural strategies for coordinated and reliable agent behavior.
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)
Harald Bontjer
Last position:
Lecturer AI Manager in Insurance at Deutsche Versicherungsakademie DVA
- Lecturer for the AI Manager in Insurance certification course
Oussama El Allam
Last position:
Head of R&D at eXagotec GmbH
- Spearheading multidisciplinary engineering teams in the development of next-generation medical devices
- Orchestrating research initiatives and technology roadmaps to deliver innovative medical solutions
- Overseeing R&D budget and managing project portfolios from concept through to commercialisation
- Establishing strategic collaborations with clinical partners for technology validation
Discover over 15,000 top freelancers
Statistics of experts using Fine-Tuning
Aggregated from the professional profiles of matched freelancers.
Experience
16 years (Germany: 13 years)
Position duration
1.7 years (Germany: 2 years)
Positions per freelancer
11 (Germany: 9)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Automotive, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
94% (Germany: 96%)
Master's degree or higher
88% (Germany: 75%)
Doctorate
44% (Germany: 19%)
Certifications per freelancer
3
Most common languages
English, German, French
Speak two or more languages
100% (Germany: 97%)
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 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 Fine-Tuning
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 it does
Fine-Tuning adjusts a base model to perform better on a specific task, domain, or style. It is used when a general model is close, but not accurate enough for real work. Teams use it to shape outputs for support, search, classification, extraction, and branded language.
Common setups
- Supervised fine-tuning for task-specific behavior
- Instruction tuning for better prompt following
- Parameter-efficient methods such as LoRA and QLoRA
- Evaluation sets for quality and regression checks
These projects often sit beside OpenAI fine-tuning, Hugging Face workflows, or self-hosted model stacks. In Munich, specialists are often brought in for enterprise systems that need German language handling, strict terminology, or careful review before release.
When companies hire
Companies bring in freelance expertise when a base model is inconsistent, expensive to prompt, or weak on internal terminology. They also need help when they want to move from experiments to a stable workflow with repeatable results. A strong specialist can tell whether fine-tuning is the right step, or whether retrieval, prompt design, or better data is enough.
Data and evaluation
Good Fine-Tuning work starts with clean, representative data. The specialist reviews labels, removes noise, and checks whether the target behavior is really learnable from the examples provided. They also define evaluation prompts, test sets, and review loops so quality does not depend on a few lucky samples.
Skills that matter
- Prompt design and dataset preparation
- Model evaluation and error analysis
- Workflow knowledge across OpenAI, Hugging Face, and APIs
- Deployment awareness for latency, cost, and versioning
Strong professionals explain trade-offs clearly and document what changed, why it changed, and how to roll it back. They work well with product, data, and engineering teams, especially when the model must fit existing systems or compliance rules.
Munich projects
Munich companies often need Fine-Tuning for internal assistants, document workflows, customer support, and industry-specific text generation. Many teams prefer a mix of remote work and short on-site sessions for kickoff, review, or data workshops. The best experts keep the scope tight and focus on measurable output quality, not model size alone.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Fine-Tuning.
Fine-Tuning is used to adapt a model so it handles a specific task, tone, or domain more reliably. Companies use it for classification, extraction, support replies, structured output, and terminology-heavy text. It is useful when prompting alone does not give stable results.
Fine-Tuning changes the model itself, while prompt engineering changes the instructions around it. Prompting is faster to test and easier to update, but it can be brittle. Fine-tuning is better when the same behavior has to hold across many inputs and the data is consistent enough to teach it.
A strong fine-tuning project solves behavior, not just knowledge. If the model needs better style, formatting, classification, or domain-specific responses, fine-tuning can help. If the main issue is access to fresh or private content, retrieval augmented generation is often the better first step.
A strong Fine-Tuning specialist usually knows data preparation, prompt design, and evaluation. Familiarity with Hugging Face, OpenAI fine-tuning, and API-based deployment is common. It also helps if they understand basic scripting, versioning, and how to review model outputs with non-technical stakeholders.
Fine-Tuning works best when the target behavior is clear and you already have examples of good and bad outputs. If the use case is still changing every week, the data will age quickly and the effort may be wasted. Many teams first stabilize the workflow, then fine-tune once the pattern is proven.
Yes, Fine-Tuning is often handled remotely, especially for dataset review, evaluation, and iterative model checks. Munich teams may still want on-site sessions for sensitive data, stakeholder alignment, or workshop-style work. A good freelancer can switch between both without slowing the project down.
Look for someone who talks about data quality, evaluation, and failure cases, not just model names. A good Fine-Tuning expert can explain why a model improved, where it still fails, and how they would test the next version. Clear documentation and repeatable experiments matter more than flashy demos.
Fine-Tuning is the general approach, while OpenAI fine-tuning and LoRA are specific ways to do it. OpenAI fine-tuning is common when teams already use that ecosystem, and LoRA or QLoRA are popular in open-source workflows. The right choice depends on cost, control, and where the model will run.
The average hourly rate of freelancers in Munich, Germany who have used Fine-Tuning in their recent projects is 98 €, which corresponds to a daily rate of about 787 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Fine-Tuning in their recent projects, 94% hold at least a Bachelor's degree, 88% hold at least a Master's degree, and 44% hold a doctorate.
On average, freelancers in Munich, Germany who have used Fine-Tuning in their recent projects have 16 years of professional experience, with a single engagement typically lasting around 1.7 years.
The most common languages among freelancers in Munich, Germany who have used Fine-Tuning in their recent projects are English (100%), German (94%), and French (17%).
The most common industries among freelancers in Munich, Germany who have used Fine-Tuning in their recent projects are Information Technology (89%), Automotive (56%), and Banking and Finance (44%).
The most common business areas among freelancers in Munich, Germany who have used Fine-Tuning in their recent projects are Information Technology (100%), Product Development (83%), and Business Intelligence (72%).
Main locations of FRATCH Experts, who have recently used Fine-Tuning
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.
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Philipp Thomaschewski
FRATCH CEO
