Fine-Tuning Experts in Berlin
from over 15,000 CVs with precise AI matchingHire experts who adapt language models to your domain, build reliable training datasets and evaluate model behavior in production. FRATCH matches you quickly with vetted, available freelancers who fit your technical and project needs.
Meet FRATCH Experts in Berlin, who have recently used Fine-Tuning
Stefan O.
Last position:
Founder at ProtocolEngine.io
Evidence-led health intelligence platform turning published research into personal health protocols. It scores 430 habits, foods, and supplements against the studies behind them, and moves the score when the evidence moves. Built solo.
- Built the daily ingestion pipeline across PubMed, bioRxiv, and medRxiv: 43,000+ papers from 3,400+ journals processed into 230,000+ typed evidence claims, each one traceable back to the study it came from.
- Designed the six-factor evidence scoring model and the public changelog behind it, so no recommendation ever appears without the papers underneath it. 23,000+ grade changes recorded and explained to date.
- Shipped an entity information model connecting every intervention to its mechanisms, biomarkers, and outcomes: 118 biomarkers with region-specific reference ranges, 77 mechanisms, 32 graded outcomes.
- Built the personalisation layer: blood panel ingestion that reads lab PDFs with a vision model and corrects results for draw time against the user's wake anchor, plus Oura, WHOOP, and Withings integration for daily readiness context.
- Operate eleven specialised review agents over the corpus and codebase, covering paper curation, retrieval quality, health-claim compliance across EU and US regimes, and security.
- Shipped the Evidence Assistant, a RAG assistant that answers from the claim database and cites the underlying papers, plus a B2B practitioner tier, an Expo React Native app, and localisation across 3 languages and 7 markets.
Stack: Next.js 16, TypeScript, Supabase, pgvector, Anthropic Claude, Vercel, DeepInfra.
Gilad G.
Last position:
European Strategy Atlas – Independent Analytics & Decision-Support Project at Independent Project
Designed and built an end-to-end interactive decision-support application using public European data across 27 EU countries and multiple strategic dimensions. Developed a structured analytical methodology for comparing countries, identifying patterns and trade-offs, and exploring strategic choices rather than presenting static dashboards. Translated complex multidimensional data into guided interactive exploration and learning workflows for non-specialist users. Built the application end-to-end using Python and Streamlit, with AI-assisted development and Git-based version control. Developed the project independently from problem framing and data analysis through methodology, UX logic, implementation and deployment.
Tools: Python, Streamlit, Git, AI-assisted development
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Muzamal A.
Last position:
Data Scientist / AI Consultant at HelmX
- Delivered AI and data science solutions, including LLM-based chatbots and data pipelines, improving operational efficiency.
- Collaborated on product features, achieving measurable impact and maintaining strong client relationships.
Hamza K.
Last position:
Academic Research Contributor in Health Sector (Volunteer)
- Acted as technical consultant to optimize multi-layer ensemble models combining ResNet, CNN-BiGRU-Attention, and XGBoost.
- Guided implementation of a Logistic Regression meta-learner to solve class imbalance problems, achieving 92.86% accuracy and 0.9644 AUC on PTB-XL and Chapman-Shaoxing datasets.
Ben S.
Last position:
AI Trainer & Data Annotator at Scale AI
- RLHF & Model Evaluation: Evaluated, ranked, and refined Large Language Model (LLM) responses based on accuracy, reasoning quality, safety guidelines, and factual correctness.
- Data Annotation & Prompt Engineering: Created high-complexity prompts, edge-case scenarios, and gold-standard reference answers to train and fine-tune generative AI models.
- Quality Assurance & Verification: Conducted rigorous fact-checking, logical consistency validation, and multi-turn response optimization.
Ersin K.
Last position:
Founder & Lead Architect at ORBYNT / 7Style
- Full automation of the software development process: ticket analysis → AI coding agents → pull request → automated code review → deployment
- Multi-tenant architecture with 82 database models and real-time WebSocket monitoring
- Integration of 40+ AI tools with Claude & GPT
- Tech stack: React, TypeScript, Express.js, PostgreSQL, Redis, BullMQ
- Platform in productive use with paying customers
Amar Sankar K.
Last position:
Prompt & Eval Playbook for CRM Conversations (Personal)
- Designed a compact framework to generate prompt–response sets for CRM lifecycle scenarios (onboarding, activation, retention, reactivation).
- Included adversarial variants (ambiguous requests, conflicting instructions, policy traps).
- Created a scoring rubric for factuality, tone, and coherence.
- Developed a lightweight guideline for annotator alignment and disagreement resolution.
Oleg A.
Last position:
Staff Software Engineer at Kpler Germany GmbH
- Delivered a new notifications platform implementation built from scratch to replace existing and upcoming services
- Collaborating with other teams to integrate more domains
Tech stack:
- Data: Scala 3, Apache Kafka, Python, Airflow, Astronomer
- BE-FE: TypeScript, NestJS, Java, Spring Boot, Vue
- Dev-ops: AWS, PostgreSQL, Docker, GitHub Actions, Kubernetes, Helm, ArgoCD
Ludvig G.
Last position:
Founder at Insightl.ai Lernplattform
- Attempted founding of a platform for career development and personal coaching
- Top 3 placement in the Berlin-Brandenburg business plan competition
- Conducted independent market analysis and user research
- Built a comprehensive knowledge graph for roles, skills, and experiences
- Data transformation and setting up data pipelines on Azure
Yaswanth R.
Last position:
Associate Software Developer at Buzzing Bulbs Private Limited
- Designed and developed image classification models for computer vision tasks, including dataset creation, preprocessing, validation, and visualization; applied machine learning and deep learning techniques to optimize accuracy and performance.
- Conducted experiments with appropriate ML algorithms, LLM-based approaches, and tools; performed statistical analysis, hyperparameter tuning, and fine-tuning using test results for robust model performance.
- Developed and optimized CRUD operation APIs using Node.js (Fastify) and Flask (Python), improving latency and ensuring scalability.
- Implemented backend solutions with SQLAlchemy for database management, and automated workflows using cron jobs and AWS Lambda functions.
- Experienced in implementing and maintaining CI/CD pipelines to streamline deployments and ensure reliable software delivery.
- Consistently achieved service time and quality targets while maintaining strong, collaborative, and professional working relationships across teams.
Vijay S.
Last position:
Head of Digital Product at Allride GmbH
- Defined and executed the end-to-end vision for Allride’s core mobility product, launching a fully functional mobile app on iOS and Android in under 3 months.
- Led product from 0 to 1 crafting roadmap, aligning teams, and driving execution to meet user needs and business objectives.
- Built a tiered S/M/L/XL subscription model based on mobility usage, launching the first MVP with built-in rewards and coupons to drive adoption of Allride’s recurring plans.
- Designed and implemented a high-conversion referral program that rewarded both referrers and invitees, driving 12% of new user acquisition through organic growth loops.
- Developed and launched Allride for Work, a B2B mobility benefit solution that enabled companies to offer sustainable commute plans to employees, driving corporate adoption and unlocking a new recurring revenue stream.
- Aligned product initiatives with sustainable mobility goals, contributing to rapid growth and early media coverage.
Fares K.
Last position:
Research Assistant – AI & Computer Vision at Iris-Sensing GmbH
- Designed and implemented a real-time perception pipeline using YOLOv7 on Time-of-Flight (ToF) sensor data, enabling live streaming, inference, and on-frame visualization for passenger detection.
- Fine-tuned and evaluated multiple state-of-the-art monocular depth estimation models for Automatic Passenger Counting (APC), and developed a custom hybrid depth model that improved depth accuracy in challenging scene regions.
- Demonstrated that model-generated depth maps outperform raw sensor depth for APC tasks across several datasets, contributing to measurable reductions in counting error.
Kashaf K.
Last position:
AI Consultant / Expert at Siemens Mobility
- Evaluated 45+ AI use cases and developed a prioritization framework for Siemens’ internal AI roadmap.
- Tested internal tools like DRIM, SiemensGPT, Microsoft Copilot; presented evaluation outcomes to stakeholders.
- Identified performance gaps and improved tool adoption by 65%.
- Supported AI knowledge-sharing initiatives, led tool onboarding sessions, and improved team AI literacy.
- Collaborated with engineering, procurement, and digital teams on tool feedback and strategy alignment.
Muskan V.
Last position:
AI Engineer at Sagas IT Analytics
- Built an AI Research Assistant with RAG, LangChain, LangGraph, and OpenAI LLMs integrated with vector search; cut research time by 30%.
- Designed custom retrieval workflows with LlamaIndex, building a ReAct-style agent for dynamic chunking; improved query accuracy by 18%.
- Researched and optimized embedding strategies, reducing retrieval cost/query by 15%.
- Developed RAG evaluation frameworks using RAGAS and Langsmith with custom datasets; improved coverage by 40%.
- Fine-tuned LLMs (LLaMA 2 on Vertex AI with custom inference containers, dynamic batching, and quantization); reduced inference latency by 25%.
- Integrated AI agents in LangGraph with short-term & long-term memory (Mem0); increased task completion rate by 20%.
- Created schema-aware synthetic data generators; fine-tuned downstream models achieving +12% F1 score.
Shyam Sundar R.
Last position:
GenAI Engineer at Freelance
- Built a hybrid semantic and keyword search and LLM-based requirement extraction from conversational queries, boosting search accuracy by 85%, cutting zero-result searches by 70%, and reducing search time by 60%.
- Deployed a production-ready API with monitoring dashboards over 100K+ products, keeping response times under 2s and reducing customer search-to-purchase time by 40%.
- Technologies: Python, BGE-M3, Qwen2.5, FastAPI, Qdrant, Meilisearch, Docker, Prometheus, vLLM.
Discover over 15,000 top freelancers
Statistics of experts using Fine-Tuning
Aggregated from the professional profiles of matched freelancers.
Experience
9 years (Germany: 11 years)
Position duration
2.3 years (Germany: 1.9 years)
Positions per freelancer
7 (Germany: 8)
Top business areas
Product Development, Information Technology, Research and Development
Top industries
Information Technology, Automotive, Education
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
88% (Germany: 98%)
Master's degree or higher
63% (Germany: 78%)
Doctorate
13% (Germany: 21%)
Certifications per freelancer
2 (Germany: 3)
Most common languages
English, German, Russian
Speak two or more languages
88% (Germany: 97%)
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 Berlin 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 Berlin 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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Fine-Tuning 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 (88%)
- Automotive (44%)
- Education (38%)
- Healthcare (38%)
- Banking and Finance (31%)
- Media and Entertainment (31%)
- Retail (31%)
- Manufacturing (25%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Model Adaptation
Fine-tuning adapts a pretrained language model to a specific domain, task or response style. Specialists train the model further with carefully prepared examples instead of building a foundation model from scratch. The result can be more consistent outputs for support, classification, extraction, generation or internal knowledge workflows.
Training Methods
The right method depends on the model, data and quality target. Supervised fine-tuning, often called SFT, uses labeled instruction and response pairs, while instruction tuning focuses on following user requests more reliably. Parameter-efficient methods such as LoRA and QLoRA can reduce the resources needed for adaptation without changing the whole model.
Data And Tooling
Fine-tuning work depends on data quality as much as model selection. Specialists clean and structure datasets, remove duplicates, protect sensitive content and define training, validation and test splits. They may work with Hugging Face Transformers, PEFT, TRL, PyTorch, cloud GPU services and experiment tracking tools.
Practical Use Cases
Companies bring in fine-tuning expertise when a general model does not meet their domain or process requirements. Typical deliverables include:
- Domain-specific text classification and extraction
- Consistent tone for customer or internal communication
- Structured outputs for business workflows
- Instruction-following models for specialized assistants
- Evaluation pipelines for quality, safety and regressions
When To Hire
Freelance specialists are useful when a team needs to test whether fine-tuning is better than prompt engineering, retrieval-augmented generation or a smaller purpose-built model. They can define a sound baseline, prepare training data, select an adaptation method and connect experiments to deployment. In Berlin, collaboration may combine remote delivery with on-site workshops when product, data and compliance teams need close alignment.
What Strong Specialists Do
Strong professionals treat fine-tuning as a complete machine learning workflow, not just a training run. They understand tokenization, learning behavior, evaluation design, inference costs and model serving, and they document why each decision was made. They also check for data leakage, unsafe outputs, overfitting and performance changes across real user scenarios. Clear communication matters when subject-matter experts must review examples and acceptance criteria.
Frequently asked questions
Quick answers to the questions that come up most around Fine-Tuning.
Fine-Tuning adapts a pretrained language model to a focused task, domain or communication style. Companies use it for classification, extraction, structured generation, specialized assistants and consistent responses where prompting alone is not reliable enough.
Fine-Tuning changes a model’s behavior through training, while prompt engineering changes the instructions supplied at runtime and retrieval-augmented generation adds relevant external context. Fine-tuning is useful for stable behavior and output formats; RAG is usually better when answers must reflect changing documents or source-grounded knowledge.
A strong Fine-Tuning specialist should understand dataset design, Python, PyTorch, tokenization, evaluation and model serving. Experience with Hugging Face Transformers, PEFT, LoRA, cloud GPU environments and retrieval systems is also valuable when the adapted model must operate in a complete application.
Fine-Tuning work requires more than familiarity with training commands. The specialist should have handled comparable data, selected suitable baselines, measured quality against real tasks and investigated issues such as overfitting, leakage and unstable outputs. The required depth depends on model access, data sensitivity and production risk.
Fine-Tuning projects can usually be delivered remotely because datasets, experiments and model artifacts are managed digitally. Berlin-based teams may still prefer on-site workshops for data review, product alignment or security discussions, while language expectations should be agreed before the engagement starts.
Useful Fine-Tuning data is representative, legally usable and closely aligned with the target behavior. Examples should show the desired inputs and outputs, cover meaningful edge cases and be reviewed for duplicates, sensitive information, inconsistent labeling and contamination between evaluation and training material.
Supervised fine-tuning is a practical form of instruction tuning when a company has examples pairing requests with preferred answers or labels. It can improve task adherence and output structure, but a specialist should first test prompting and retrieval baselines to confirm that training is justified.
A quality Fine-Tuning engagement has clear acceptance criteria, a documented baseline and evaluation data that reflects real usage. Review not only average quality but also failures, safety behavior, consistency, latency, serving requirements and how well the model generalizes beyond the examples it saw during training.
The average hourly rate of freelancers in Berlin, Germany who have used Fine-Tuning in their recent projects is 87 €, which corresponds to a daily rate of about 695 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Fine-Tuning in their recent projects, 88% hold at least a Bachelor's degree, 63% hold at least a Master's degree, and 13% hold a doctorate.
On average, freelancers in Berlin, Germany who have used Fine-Tuning in their recent projects have 9 years of professional experience, with a single engagement typically lasting around 2.3 years.
The most common languages among freelancers in Berlin, Germany who have used Fine-Tuning in their recent projects are English (100%), German (88%), and Russian (13%).
The most common industries among freelancers in Berlin, Germany who have used Fine-Tuning in their recent projects are Information Technology (88%), Automotive (44%), and Education (38%).
The most common business areas among freelancers in Berlin, Germany who have used Fine-Tuning in their recent projects are Product Development (100%), Information Technology (94%), and Research and Development (69%).
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.
Munich 
Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
