LLMOps Experts in Berlin
in minutes from over 15,000 CVs with the power of AI.Hire experts who can run prompt versioning, model monitoring, evaluation pipelines, and safe deployment for LLM apps. Bring in specialists for retrieval workflows, guardrails, and production support, with fast, precise matching from vetted, available freelancers.
Meet FRATCH Experts in Berlin, who have recently used LLMOps
Haseeb Z.
Last position:
Senior Data Scientist at WPP MEDIA
- Designed and deployed enterprise Retrieval-Augmented Generation (RAG) applications using LangChain, LangGraph, vector databases, embeddings, and open-source LLMs served through vLLM on GCP GPU infrastructure.
- Built agentic AI workflows using LangGraph with planning, reasoning, tool execution, persistent memory, session management, and Human-in-the-Loop approval mechanisms.
- Developed LLM-powered automation systems integrating BigQuery, SQL pipelines, and external advertising APIs including Meta, TikTok, Amazon, Snapchat, Google, and Pinterest, reducing manual operational workflows.
- Architected multi-agent AI systems for enterprise analytics and decision-support workflows, enabling autonomous task execution and intelligent data interactions.
- Implemented retrieval optimization strategies including multi-retriever architectures, semantic search, context optimization, and query improvement techniques, improving response relevance by approximately 40%.
- Engineered structured prompting strategies, function-calling schemas, and validation workflows to improve reliability of multi-step LLM applications.
- Designed scalable AI services using Python, FastAPI, Cloud Run, Pub/Sub, BigQuery, Docker, and cloud-native deployment architectures.
Sunish B.
Last position:
AtlasMind - Production AI assistant for Jira at Mercedes Benz Innovation Labs Gmbh
- Converts natural language into JQL using RAG and pgvector. Returns structured JSON with a query, chart spec, and plain-text answer. A two-stage router answers general questions without touching the JQL pipeline at all.
- Interchangeable LLM backends: Ollama, vLLM, Groq, Anthropic Claude, AWS Bedrock - switchable at runtime, no code changes. Self-healing JQL: on Jira validation failure, feeds error back to LLM, retries up to 4 times. OCI Vault for secrets. Deployed on Oracle Cloud A1 with GPU inference over Tailscale private network. Open source.
Wolfram K.
Last position:
AI / Machine Learning Engineer (Projects & Applied AI) at UNIVERSITÉ PARIS 1 PANTHEON-SORBONNE & LIORA
- Designed and implemented a hybrid recommendation system (content-based + collaborative filtering)
- Built end-to-end ML pipelines including data processing, feature engineering, model training, and evaluation
- Developed RAG-based LLM systems using LangChain and vector databases for semantic search and knowledge retrieval
- Established MLOps workflows with MLflow for experiment tracking, versioning, and deployment readiness
- Implemented deep learning models (computer vision & classification) using PyTorch and TensorFlow
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.
Unnikuttan V.
Last position:
Managing Director (Co-Founder) at AathmaSignals
- Spearheading investor outreach and partnership development as founding MD, building the business case and technical narrative needed to attract initial funding and strategic collaborators in the digital health space
- Designing multi-agent AI systems for autonomous biosignal analysis, orchestrating LLM-based reasoning pipelines with domain-specific medical context to enable intelligent, clinical decision support
Louis G.
Last position:
Freelance Solutions Architect and Machine Learning Engineer at Self-employed
- Develop and demonstrate solutions using GenAI software like langchain, vercel ai sdk, copilotkit
- Work with customers to understand their challenges and provide the best solutions based on open-source data products
- Build RAG and GraphRAG solutions using Neo4j, lancedb, and Postgres
- Deploy a LLMOps platform using kubernetes, terraform, helmfile, Arize phoenix, mlflow
- Architect and build data pipelines using dbt, Trino, Spark, Iceberg, Airflow, ArgoCD, terraform, kubernetes
- Delivered user-centred technical strategy for Agriculture 4.0 and precision livestock farming, helping my client secure funding from Bpifrance
- Delivered a prospecting tool for a leading French solar carport installer, using geospatial computing (GIS), speeding up the sales process
- Built digital twin architecture for solar carports and EV chargers, making real-time monitoring and smart charging possible
Discover over 15,000 top freelancers
Statistics of experts using LLMOps
Aggregated from the professional profiles of matched freelancers.
Experience
14 years
Position duration
2 years
Positions per freelancer
7
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Healthcare, Automotive
Certification focus areas
Information Technology, Product Development, Research and Development
Bachelor's degree or higher
100%
Master's degree or higher
67%
Certifications per freelancer
3
Most common languages
English, German, French
Speak two or more languages
83%
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 LLMOps
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.
LLMOps 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%)
- Healthcare (67%)
- Automotive (50%)
- Media and Entertainment (50%)
- Professional Services (50%)
- Education (33%)
- Energy (33%)
- Advertising (17%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What LLMOps covers
LLMOps is the set of practices for building, shipping, and running large language model systems in production. It covers prompt design, retrieval-augmented generation, evaluation, observability, and release control for chat, search, and automation features.
Typical work
- Prompt and template management
- RAG pipelines and vector search
- Offline and online evaluation
- Safety, guardrails, and content filtering
- Monitoring for drift, latency, and cost
Tooling stack
Strong professionals know the LLM stack around the model itself: orchestration frameworks, vector databases, feature stores, tracing, experiment tracking, and deployment tooling. They also understand how OpenAI, Anthropic, and open-source models fit into a secure delivery flow.
When to bring in help
Companies usually look for freelance expertise when an LLM prototype needs to become stable, auditable, and usable by real teams. That is common in Berlin product teams, SaaS vendors, and enterprises that need multilingual support, data controls, and a clear release process.
What good specialists do
Good specialists do more than write prompts. They measure answer quality, reduce hallucinations, design fallback paths, and make sure retrieval, caching, and logging work together.
Signs you need LLMOps
- Answers change after small prompt edits
- Teams cannot explain why output quality changed
- Costs rise without a clear cause
- Release reviews lack clear evaluation evidence
- Security or privacy concerns block rollout
Frequently asked questions
Key details about LLMOps, drawn from the questions we get asked most.
LLMOps is used to run large language model applications in a controlled way after the first prototype works. It helps teams manage prompts, retrieval, evaluation, logging, and safe releases for chat tools, knowledge assistants, and workflow automation.
LLMOps overlaps with MLOps, but it has extra focus on prompt behavior, retrieval quality, and human-reviewed evaluation. Traditional MLOps is often centered on training and serving predictive models, while LLM work also needs guardrails, context control, and output checking.
A strong LLMOps specialist usually works across orchestration, vector databases, tracing, and evaluation tooling. Common adjacent pieces include LangChain, LlamaIndex, OpenAI or Anthropic APIs, and observability tools that show prompts, responses, and failure patterns.
Look for someone who has shipped production systems, not just demos. A strong LLMOps freelancer can explain how they test prompts, measure answer quality, reduce hallucinations, and add fallback logic when the model is uncertain.
Many LLMOps tasks can be handled remotely because the work is mostly in code, evaluation, and system design. On-site collaboration in Berlin can help when access to sensitive data, internal reviews, or cross-team workshops make faster decisions easier.
LLMOps work often needs Python, API integration, cloud infrastructure, and data engineering basics. Security review, prompt design, and product thinking matter too, because the specialist has to connect model behavior to real business workflows.
LLMOps work improves fast when the specialist gets access to sample prompts, target tasks, known failure cases, and the current evaluation approach. Without that context, it is hard to tell whether the issue is the model, the retrieval layer, or the prompt design.
LLMOps quality shows up in the system, not in the slide deck. Ask for clear tests, tracked changes, reproducible evaluations, and a simple explanation of trade-offs around latency, cost, safety, and answer quality.
The average hourly rate of freelancers in Berlin, Germany who have used LLMOps in their recent projects is 95 €, which corresponds to a daily rate of about 760 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used LLMOps in their recent projects, 100% hold at least a Bachelor's degree and 67% hold at least a Master's degree.
On average, freelancers in Berlin, Germany who have used LLMOps in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers in Berlin, Germany who have used LLMOps in their recent projects are English (100%), German (83%), and French (33%).
The most common industries among freelancers in Berlin, Germany who have used LLMOps in their recent projects are Information Technology (100%), Healthcare (67%), and Automotive (50%).
The most common business areas among freelancers in Berlin, Germany who have used LLMOps in their recent projects are Information Technology (100%), Product Development (100%), and Research and Development (100%).
Main locations of FRATCH Experts, who have recently used LLMOps
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.
Countries:
Berlin 
Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
