DeepEval Experts in Germany
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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) at n/a
- 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 – conception, architecture, development, and production operations as the sole developer; also customer discussions, proposals, and marketing.
Underwriting Copilot – AI assistant for industrial insurance (in production with a customer)
- Supports underwriters in analyzing industrial insurance submissions – in production at an industrial insurer.
- Framework-independent RAG architecture with Hybrid Search (BM25 + pgvector) across large, heterogeneous document collections.
- 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 with a customer)
- Automatically collects and analyzes food 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.
- Complete CI/CD pipelines (GitHub Actions), production cloud deployment, interactive dashboards (Dash).
Kaiki GEO Atlas – GEO platform (in production with a customer)
- 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 additional LLM effort).
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 development)
BankingGPT – AI assistant for complaint management in cooperative banking
- Security architecture at its core: no AI draft reaches the customer without human approval – the approval decision is stored in auditable code, not in the language model (monotonic: the model may increase the level, but never lower it).
- Genuine agentic components, each with its own boundaries: the model selects tools itself through an MCP server (read-only, allowlisted, capped, fail-safe); sensitive cases are sent via the open A2A protocol (JSON-RPC, Agent Card, message/send/tasks/get; client implemented independently) to a standalone specialist agent (securities/law), which never lowers the review requirement (pinned by tests).
- Evaluation-driven across ten analysis rounds; an independent review and blind tests uncovered a security defect that nine automated runs had missed.
- Voice AI frontend, responding live: covered cases are answered during the conversation, while sensitive cases are escalated before generation; measured response latency < 7 s (local GPU STT/TTS).
Stack & production readiness: Python, pydantic-ai, FastAPI/Celery, PostgreSQL/pgvector, FastMCP, fasta2a, Docker; multi-tenant (physical vector isolation per tenant), encrypted PII, OWASP-LLM-reviewed, 275 tests, CI/CD; provider-portable (Ollama / EU-Cloud-Vertex).
After-Sales Assistant – agentic RAG/GraphRAG assistant based on public OEM manuals (automotive after-sales)
- Genuinely agentic with LangGraph: ReAct agent with four tools and conversation memory – the model decides itself whether to use the manual (RAG, Chroma), a knowledge graph (GraphRAG, Neo4j/Cypher – decoding warning lights), or a workshop/booking service.
- Human-in-the-loop before the irreversible action: before every appointment booking, the graph pauses (interrupt) and obtains the driver's explicit confirmation – the same approval-before-action discipline as in BankingGPT, in a different framework.
- Eval as a CI gate: three-part scorecard (RAGAS grounding + deterministic tool-routing accuracy + DeepEval safety: does the answer mention the warning first in the case of 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
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
Igor K.
Last position:
Freelance Software Developer
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, Research and Development
Top industries
Information Technology, Professional Services, Banking and Finance
Bachelor's degree or higher
100%
Master's degree or higher
67%
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 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging 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 29 Oct 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 (86%)
- Banking and Finance (71%)
- Education (57%)
- Automotive (43%)
- Energy (43%)
- Healthcare (43%)
- Manufacturing (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What DeepEval does
DeepEval is an open-source evaluation framework for large language model applications. It helps teams test outputs against criteria such as factuality, relevance, answer completeness, safety, and retrieval quality. Companies use it to replace informal prompt checks with repeatable evaluation workflows for chatbots, agents, and retrieval-augmented generation systems.
Core evaluation work
Specialists define test cases, select suitable metrics, and compare model or prompt changes against a trusted baseline. They can evaluate both the final answer and intermediate steps, including retrieved context, tool calls, and reasoning traces where those are available. The goal is to detect regressions before an LLM feature reaches users.
- Create datasets from production-like questions
- Measure hallucination, faithfulness, and answer relevance
- Test retrieval-augmented generation pipelines
- Add evaluations to pull requests and release workflows
Ecosystem and tooling
DeepEval is commonly used with Python-based application stacks and model providers such as OpenAI, Anthropic, and Azure OpenAI. It can sit alongside LangChain, LlamaIndex, vector databases, tracing tools, and continuous integration services. Strong specialists understand how evaluation results connect to prompts, retrieval settings, model selection, and application logs.
When companies need specialists
Freelance expertise is useful when an internal team has an LLM prototype but no dependable way to judge quality at scale. It also helps during model migrations, prompt redesigns, RAG tuning, and the rollout of agent workflows. In Germany, remote collaboration is common, while on-site workshops can help product, compliance, and data teams agree on evaluation criteria.
- Establish a practical evaluation strategy
- Turn user feedback into measurable test cases
- Investigate inconsistent or unsafe model outputs
- Connect evaluation results with delivery decisions
What strong professionals deliver
A capable DeepEval professional links each metric to a real product risk instead of treating scores as the goal. They design representative datasets, explain false positives, review evaluator prompts, and separate model failures from retrieval or application defects. They also document thresholds and make results understandable to product and engineering stakeholders.
Selecting the right fit
Look for evidence of shipped LLM evaluation work, not only familiarity with the package. Ask how the specialist handled changing model behavior, limited ground truth, sensitive data, and disagreement between automated metrics and human review. For German teams, clarify language coverage, data-handling expectations, and whether the professional can work effectively across remote or on-site settings.
Frequently asked questions
What clients ask us most about DeepEval — answered in short.
DeepEval is used to test and monitor the quality of LLM applications such as chatbots, RAG systems, and tool-using agents. It supports metrics for factuality, relevance, faithfulness, context quality, and other application-specific criteria. A specialist can turn these checks into repeatable tests for development and release workflows.
DeepEval automates repeatable checks, while human evaluation remains important for nuanced quality, tone, and safety judgments. Compared with general observability tools, it focuses more directly on evaluation cases and metric-based testing. Teams often combine it with tracing, dataset tools, or provider-specific evaluation services rather than treating it as a complete replacement.
A strong DeepEval specialist should understand Python, prompt design, model APIs, retrieval-augmented generation, embeddings, and vector databases. Experience with LangChain or LlamaIndex can help when those frameworks are part of the application. Knowledge of CI pipelines, tracing, data privacy, and human review makes the evaluation work more useful in production.
The right DeepEval experience depends on the project’s risk and system complexity. A focused test suite for a prototype needs less depth than an evaluation program covering agents, multilingual content, sensitive data, and production releases. Ask candidates to explain how they choose datasets, validate metrics, and investigate disagreement between automated results and user feedback.
DeepEval is designed for LLM applications rather than one exclusive model provider. Specialists can usually adapt evaluations to applications using providers such as OpenAI, Anthropic, or Azure OpenAI, as well as frameworks such as LangChain and LlamaIndex. The exact integration depends on the application’s inputs, outputs, retrieval flow, and logging setup.
Yes, DeepEval work is well suited to remote collaboration because test cases, datasets, metric definitions, and CI results can be reviewed asynchronously. German teams should agree early on working language, access to sensitive prompts and documents, review routines, and the balance between remote delivery and on-site workshops. Clear documentation is especially important when product and compliance stakeholders are distributed.
Evaluate DeepEval work by checking whether the tests reflect real user risks and whether the results lead to clear engineering decisions. A quality specialist explains metric limitations, maintains representative datasets, investigates failures, and avoids relying on a single score. Look for transparent documentation and examples of improvements verified through both automated and human review.
DeepEval can support multilingual evaluation when the test data, criteria, and evaluator setup reflect the languages used by the application. For German-language systems, ask the specialist to test German prompts, source documents, terminology, and answer quality rather than assuming English results transfer directly. Human review may be needed for regional phrasing, domain language, and culturally sensitive responses.
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 754 € 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, 67% 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 (86%), 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 Research and Development (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.
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