LLMOps Experts in Berlin
to run reliable language-model systems, matched with vetted and available freelancers in minutesHire experts who design evaluation pipelines, manage retrieval-augmented generation and control production inference across cloud and private infrastructure. Get fast, precise matching with vetted, available freelancers for your LLMOps project.
Meet FRATCH Experts in Berlin, who have recently used LLMOps
Mukund B.
Last position:
Voice AI Chatbot - Real-Time Audio Assistant
- ▶ Built real-time voice assistant (STT → LLM → TTS pipeline) benchmarking and evaluating multiple STT providers including faster-whisper and Azure Speech. achieved sub-3s latency, Groq API (Llama 3) with multi-turn memory - directly handling edge cases in dictation, names and passcode recognition.
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.
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.
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
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.
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
1.9 years
Positions per freelancer
7
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Healthcare, Education
Certification focus areas
Information Technology, Product Development, Research and Development
Bachelor's degree or higher
100%
Master's degree or higher
86%
Certifications per freelancer
3
Most common languages
English, German, French
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 Berlin 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 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 29 Oct 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 (57%)
- Education (43%)
- Media and Entertainment (43%)
- Automotive (29%)
- Energy (29%)
- Professional Services (29%)
- Advertising (14%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What LLMOps covers
LLMOps, short for Large Language Model Operations, applies operational discipline to systems built with foundation models. It covers prompt and model versioning, evaluation, deployment, observability, cost control and safe iteration from experiment to production. Unlike conventional application delivery, teams must manage probabilistic output, changing model behavior and sensitive context.
Systems it supports
LLMOps specialists help companies deliver production systems such as customer-support assistants, document question-answering, internal search, summarization workflows and structured content services. They connect models to business data while preserving traceability and predictable behavior.
- Retrieval-augmented generation pipelines
- Agent workflows with tool access
- Prompt and response evaluation
- Model gateways and inference services
Ecosystem and tooling
The work spans hosted models and open-weight models, embedding services, vector databases, orchestration frameworks and application APIs. Strong specialists understand tools such as LangChain, LlamaIndex, MLflow, Weights & Biases, Langfuse, vLLM and Kubernetes, while selecting components according to latency, privacy and maintenance needs. They also integrate data stores, CI/CD pipelines, secrets management and monitoring.
When companies need support
Companies usually bring in freelance LLMOps expertise when a promising prototype must become a dependable service, or when existing outputs are difficult to measure and improve. Berlin teams may need specialists who can work remotely across distributed product groups or collaborate on-site when data, infrastructure and stakeholders require close coordination.
- Productionizing a proof of concept
- Building repeatable evaluation and release workflows
- Reducing inference cost or response latency
- Introducing guardrails, tracing and auditability
Skills that matter
A capable LLMOps professional combines model and prompt knowledge with software delivery, data engineering and platform operations. They define meaningful test sets, compare model responses, monitor drift and failure patterns, and create rollback paths. Experience with security, access control, data protection and human review is essential when outputs influence customers or business decisions.
Choosing the right specialist
Look for evidence of a complete operating approach, not only impressive demos. Ask how the specialist handles evaluation, prompt changes, model routing, retrieval quality, hallucinations, observability and incident response. A strong professional explains trade-offs clearly, documents decisions and can work with product, security and infrastructure stakeholders in English and, where useful, German.
Frequently asked questions
Key details about LLMOps, drawn from the questions we get asked most.
LLMOps is used to operate applications that rely on large language models in a controlled, repeatable way. It covers model and prompt releases, retrieval pipelines, evaluation, monitoring, security, cost management and production support.
LLMOps extends MLOps with concerns that are specific to generative systems. Specialists must manage prompts, token use, retrieval context, non-deterministic responses, safety checks and human evaluation in addition to data, models and deployment.
GenAIOps is a broader term that can include operational practices for image, audio and language generation. LLMOps focuses specifically on large language model applications, although the tools and governance patterns often overlap.
A strong LLMOps freelancer usually combines Python or TypeScript, API design, cloud infrastructure, CI/CD, observability and data engineering. Knowledge of vector databases, Kubernetes, LangChain or LlamaIndex, model gateways and security is useful for end-to-end delivery.
The right LLMOps experience depends on the project stage and risk. A prototype may need prompt and retrieval expertise, while a customer-facing service usually requires proven evaluation, deployment, monitoring, access control and incident-handling skills.
LLMOps work is often suitable for remote collaboration because infrastructure, evaluations and documentation are shared digitally. On-site work in Berlin can still help when specialists must align closely with security, data or product teams, and language expectations should be agreed early.
Ask an LLMOps specialist to explain how they measure answer quality, detect regressions and investigate failures. Strong professionals can show evaluation datasets, tracing, release controls and clear trade-offs rather than relying only on a polished demonstration.
A lightweight integration may be enough for an internal experiment, but LLMOps becomes important when usage, data sensitivity, quality expectations or model changes create operational risk. It provides the controls needed to run retrieval, agents or generation workflows reliably in production.
The average hourly rate of freelancers in Berlin, Germany who have used LLMOps in their recent projects is 101 €, which corresponds to a daily rate of about 807 € 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 86% 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 1.9 years.
The most common languages among freelancers in Berlin, Germany who have used LLMOps in their recent projects are English (100%), German (86%), and French (29%).
The most common industries among freelancers in Berlin, Germany who have used LLMOps in their recent projects are Information Technology (100%), Healthcare (57%), and Education (43%).
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 (86%).
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
