DeepEval Experts in Germany
in minutes with vetted freelancers and AI matchingHire experts who design LLM test suites, build evaluation pipelines, and tune guardrail checks with DeepEval and deepeval workflows. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used DeepEval
Hamza Khan
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.
Niko Karajannis
Last position:
Co-founder & AI Engineer at KAIKI GmbH
End-to-end responsibility for all products - concept, architecture, development, and production operation as the sole developer; in addition, customer meetings, proposals, and marketing.
Underwriting Copilot - AI assistant for industrial insurance (in production at customer sites)
- Supports underwriters in analyzing industrial insurance submissions - in production use at an industrial insurer.
- Framework-independent RAG architecture with Hybrid Search (BM25 + pgvector) across large, mixed document sets.
- Two-stage evaluation and observability pipeline (code assertions + LLM-as-Judge) that makes answer quality, retrieval accuracy, and citation integrity measurable in a regression-safe way.
Kaiki Menu Analyzer - Data intelligence platform (in production at customer sites)
- Automatically captures and analyzes menu data from around 25,000 German restaurants.
- Scalable 7-container architecture (FastAPI, partitioned PostgreSQL, Redis/RQ) with LLM-supported extraction of structured data from PDF, HTML, and images.
- Full CI/CD pipelines (GitHub Actions), production cloud deployment, interactive dashboards (Dash).
Kaiki GEO Atlas - GEO platform (in production at customer sites)
- Measures brand visibility across five AI engines (ChatGPT, Gemini, Perplexity, Grok, Claude), each augmented with web search, orchestrated as a DAG workflow pipeline (Dispatcher → Sub-workflows → Scoring → Report) with fail isolation.
- 6-container deployment (FastAPI, Celery, Redis, PostgreSQL); LLM cost estimation, PDF audit report, rule-based cross-signal insights (no extra LLM cost).
Data Pipeline & Analytics Platform - competitive analysis in the automotive aftermarket
- Automated data pipeline with gap analysis algorithms and role-based access control; 230+ tests.
- Backend with FastAPI, PostgreSQL, SQLAlchemy.
Product development (actively in progress)
BankingGPT - AI assistant for complaint management in cooperative banking
- Security architecture at the core: no AI draft reaches the customer without human approval - the approval decision is in auditable code, not in the language model (monotonic: the model may escalate, never downgrade).
- Real agentic building blocks, each with its own boundary: the model chooses tools itself through an MCP server (read-only, allowlist, capped, fail-safe); sensitive cases are handed off via an open A2A protocol (JSON-RPC, Agent Card, message/send/tasks/get; client implemented by me) to a separate specialist agent (securities/law), which never lowers the review requirement (pinned by test).
- Evaluation-driven over ten analysis rounds; uncovered a security flaw through independent review and blind tests that nine automated runs had missed.
- Voice AI frontend, responding live: covered cases are answered in the conversation, sensitive ones escalate before generation; response latency < 7 s measured (local GPU STT/TTS).
Stack & production readiness: Python, pydantic-ai, FastAPI/Celery, PostgreSQL/pgvector, FastMCP, fasta2a, Docker; multi-tenant capable (physical vector isolation per tenant), PII encrypted, OWASP-LLM reviewed, 275 tests, CI/CD; vendor-portable (Ollama / EU Cloud Vertex).
After-Sales Assistant - agentic RAG/GraphRAG assistant on public OEM manuals (automotive after-sales)
- Genuinely agentic on LangGraph: ReAct agent with four tools and conversation memory - the model decides on its own whether to use the manual (RAG, Chroma), a knowledge graph (GraphRAG, Neo4j/Cypher - decodes warning lights), or a workshop/booking service.
- Human-in-the-Loop before the irreversible action: before every appointment booking, the graph pauses (interrupt) and gets the driver's explicit confirmation - the same approval-before-action discipline as in BankingGPT, in a different framework.
- Eval as CI gate: a three-part scorecard (RAGAS grounding + deterministic tool-routing accuracy + DeepEval safety: does the answer mention the warning first when there is a critical warning?) blocks the pipeline; provider-agnostic (OpenAI/Azure/Anthropic), FastAPI with token streaming.
Stack: Python, LangChain/LangGraph, Chroma, Neo4j, RAGAS/DeepEval, FastAPI, Docker.
Patrik Garten
Last position:
Technical Lead Conversational AI at CANCOM
- Technical lead of a team developing agentic chatbot solutions (React, TypeScript, Python, FastAPI)
- Architecture design for multi-LLM dialog systems - focus on maintainability, UX, and autonomous execution
- Stakeholder alignment, CI/CD processes, and AI integration at enterprise level
Igor Kazarnovskiy
Last position:
Freelance Software Developer
Paul Oesterwitz
Last position:
Product Owner / Project Manager at Auditor, software vendor for German tax consultancies
- Project environment: Python, Java, Azure AI Studio & OpenAI Studio, embedding models, LLM as a judge
- Project language: German
- Project role(s): Project manager
- Project management for improving the performance of a chatbot
- Research and evaluation of approaches to improve and measure response accuracy and improve the chatbot's understanding of context
- Coordination of architecture decisions with the technical team and architects
- Coordination and transfer of research results into development tasks
Julien Look
Last position:
MLOps Engineer at SAMGEN
- Building and scaling cloud infrastructure on GCP to support a SaaS platform for industrial clients
- Designing and implementing a data-driven DevOps pipeline for streamlined deployment and CI/CD workflows
- Collaborating with Data Science team on MLOps workflow to automate integrated retraining
Discover over 15,000 top freelancers
Statistics of experts using DeepEval
Aggregated from the professional profiles of matched freelancers.
Experience
15 years
Position duration
2.5 years
Positions per freelancer
10
Top business areas
Information Technology, Product Development, Quality Assurance
Top industries
Information Technology, Professional Services, Banking and Finance
Bachelor's degree or higher
100%
Master's degree or higher
40%
Doctorate
20%
Certifications per freelancer
0
Most common languages
German, English, Greek
Speak two or more languages
83%
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 DeepEval
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
LLM evaluation
DeepEval is a framework for testing LLM outputs with repeatable checks. It helps teams measure answer quality, faithfulness, relevance, safety, and regression behavior before changes reach users. Companies use it to verify chatbots, RAG apps, copilots, and agent flows.
What experts deliver
- Test suites for prompt changes and model swaps
- RAG checks for grounded answers and retrieval quality
- Safety and hallucination review workflows
- Release gates for CI and QA pipelines
Strong specialists know how to turn vague product goals into clear eval criteria. They define datasets, expected outputs, and failure cases that match the real use of the system.
Ecosystem fit
DeepEval often sits beside Python, pytest, OpenAI-style APIs, LangChain, and vector search tools. Good experts connect evaluation runs to your existing test process, logs, and staging environment so teams can review results without extra manual work.
When to bring help in
Bring in freelance expertise when LLM behavior is changing too often, quality is hard to compare, or you need a cleaner release process. This is common in Germany when product, QA, and data teams need one shared way to judge answers in English and German.
What strong specialists do
They inspect prompts, traces, retrieval steps, and model outputs together. They also separate model errors from data issues, so teams know whether to change the prompt, the context, the retrieval layer, or the model itself.
Delivery focus
A good DeepEval expert leaves behind reusable checks, readable failure reports, and clear guidance for future releases. That makes it easier to maintain evaluation discipline as the product grows and more models, prompts, and data sources are added.
Frequently asked questions
What clients ask us most about DeepEval — answered in short.
DeepEval is used to test LLM apps in a repeatable way. Companies use it for chatbot quality, RAG validation, prompt regression checks, and safety review before release.
DeepEval gives structure where manual review is too subjective. Instead of reading outputs one by one, teams define checks for relevance, faithfulness, and harmful behavior, then run the same test set again after each change.
DeepEval is strongest in Python-based workflows, but the real requirement is not language alone. Good specialists can fit it into a broader stack that may include LangChain, vector databases, APIs, and CI tools.
A strong DeepEval specialist should understand LLM prompting, retrieval quality, error analysis, and test design. Knowledge of experiment tracking, logging, and release workflows is also important because the framework is only useful when it fits the full delivery process.
A DeepEval project works best when you can show real prompts, sample outputs, and the quality problems you want to catch. Even if the eval design is still rough, a good expert can turn that into test cases, scoring logic, and acceptance criteria.
Yes, DeepEval can be used to evaluate systems in both languages as long as the test data matches the language of the product. For teams in Germany, this is useful when a system must handle German customer queries and English internal content with the same quality bar.
Most DeepEval work can be done remotely because the core tasks are code, tests, and review sessions. On-site collaboration can help when teams need fast alignment on quality rules, data access, or cross-functional release decisions.
Look for clear thinking, not just framework knowledge. A strong DeepEval specialist can explain why a test failed, how to reduce false positives, and how to connect evaluation results to product decisions.
The average hourly rate of freelancers in Germany who have used DeepEval in their recent projects is 88 €, which corresponds to a daily rate of about 703 € based on an 8-hour working day.
Of the freelancers in Germany who have used DeepEval in their recent projects, 100% hold at least a Bachelor's degree, 40% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Germany who have used DeepEval in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 2.5 years.
The most common languages among freelancers in Germany who have used DeepEval in their recent projects are German (100%), English (83%), and Greek (17%).
The most common industries among freelancers in Germany who have used DeepEval in their recent projects are Information Technology (100%), Professional Services (100%), and Banking and Finance (67%).
The most common business areas among freelancers in Germany who have used DeepEval in their recent projects are Information Technology (100%), Product Development (83%), and Quality Assurance (83%).
Main locations of FRATCH Experts, who have recently used DeepEval
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
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
