DeepEval Experts in Germany
in minutes from over 15,000 CVs with the power of AIHire experts who test LLM apps, RAG pipelines, prompt changes, and agent workflows with DeepEval, Deep Eval, and related eval tooling. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used DeepEval
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
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.
Niko K.
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 G.
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
Paul O.
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
Igor K.
Last position:
Freelance Software Developer
Julien L.
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
2.2 years
Positions per freelancer
11
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
50%
Doctorate
33%
Certifications per freelancer
1
Most common languages
German, English, Bosnian
Speak two or more languages
86%
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 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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
DeepEval 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%)
- Professional Services (100%)
- Banking and Finance (71%)
- Automotive (57%)
- Education (57%)
- Energy (43%)
- Healthcare (43%)
- Manufacturing (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
LLM evaluation
DeepEval is used to test large language model applications before they reach users. It helps teams measure answer quality, hallucinations, faithfulness, relevance, and safety across prompts, chains, agents, and RAG flows. Strong experts use it to turn vague “works well” claims into repeatable checks.
What it covers
- RAG answer and retrieval quality
- Hallucination and faithfulness checks
- Prompt regression tests
- Agent workflow evaluation
- Safety and toxicity review
It fits products where output quality changes often and manual review is too slow.
Ecosystem and tooling
DeepEval sits in the wider LLM testing stack. Experts often connect it with Python test suites, CI pipelines, model providers, vector databases, and experiment tracking tools. They also compare it with other evaluation workflows when a team already uses internal scorecards or custom judges.
When companies bring in experts
Teams usually need freelance support when they are shipping a new chatbot, improving a RAG system, or tightening release checks after prompt changes. In Germany, this often comes up in product teams that need English and German evaluation coverage, clear handover docs, and remote collaboration that fits engineering workflows.
What strong specialists do
Strong professionals define useful metrics, write stable test cases, and reduce noisy evaluations. They know how to separate model issues from retrieval issues, tune prompts for consistent scoring, and make results readable for product and engineering teams. They also keep tests easy to maintain as the app changes.
Typical project fit
DeepEval is a good fit when a team needs confidence, not just outputs. It supports iterative work on copilots, search assistants, support bots, and internal knowledge tools. Experts can set up baselines, review failure cases, and create a practical test layer that catches regressions early.
Frequently asked questions
What clients ask us most about DeepEval — answered in short.
DeepEval is used to evaluate LLM applications such as chatbots, RAG systems, prompt workflows, and agent-driven tools. Companies use it to check whether answers are grounded, relevant, safe, and stable after changes. It is especially useful when manual review no longer scales.
DeepEval adds repeatable evaluation on top of prompt testing and manual checks. Manual review is useful for edge cases, but it is slow and hard to keep consistent. DeepEval helps teams catch regressions automatically and compare changes across versions.
A strong DeepEval specialist usually knows Python, LLM app design, retrieval pipelines, and test automation. Experience with RAG, vector search, prompt engineering, and CI workflows is also valuable. If the project uses custom judges or internal metrics, that matters too.
A DeepEval project can be small if a team only needs a test harness and a few core checks. It becomes larger when the app includes multiple prompts, retrieval steps, languages, or agent paths. The right expert should be able to scope the work around the production risks, not just the framework.
Yes, DeepEval can be used for multilingual products, including English and German setups in Germany. The main challenge is making evaluation cases reflect the language, tone, and domain of the actual product. Strong experts adapt test data and judge criteria instead of reusing one generic template.
Ask whether the DeepEval freelancer has worked on RAG, agents, or prompt regression tests similar to your product. Also ask how they handle flaky evaluations, model updates, and error analysis. Good answers focus on practical test design and maintainability, not just tool setup.
With DeepEval, quality shows up in stable checks, clear failure cases, and metrics that reflect real product behavior. Look for test cases that catch hallucinations, weak retrieval, and bad prompt changes without creating too much noise. A good deliverable is easy for your team to run and extend.
For most DeepEval work, remote collaboration is enough because the core tasks are test design, implementation, and review. On-site time can help if the team needs faster alignment on product rules or internal data access. In Germany, many projects are split between remote execution and short working sessions with the product team.
The average hourly rate of freelancers in Germany who have used DeepEval in their recent projects is 94 €, which corresponds to a daily rate of about 755 € 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, 50% hold at least a Master's degree, and 33% 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 2.2 years.
The most common languages among freelancers in Germany who have used DeepEval in their recent projects are German (100%), English (86%), and Bosnian (14%).
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 (71%).
The most common business areas among freelancers in Germany who have used DeepEval in their recent projects are Information Technology (100%), Product Development (86%), and Quality Assurance (86%).
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
