Llama Experts in Berlin
matched in minutes from over 15,000 CVsHire experts who fine-tune Meta Llama models, design retrieval-augmented generation systems and deploy reliable inference services. FRATCH connects you with vetted, available freelancers through fast, precise AI matching.
Meet FRATCH Experts in Berlin, who have recently used Llama
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.
Robin W.
Last position:
Founder & Consultant · Platform Engineering & AI Infrastructure at RootVector.ai
- Built and operate a hybrid Kubernetes platform across bare metal and cloud to validate multi-GPU workloads, security-zone isolation, and disaster recovery.
- Operate self-hosted AI coding agents in the platform's Git workflow, from issue triage to pull-request review; every change is gated by manifest diffs and policy checks in CI.
- Co-developed a sensor-fusion and GPU edge-inference platform selected by the European Defense Tech Hub from 50 solutions for field testing.
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.
Mark W.
Last position:
Independent IT/AI Consultant at Freelance
- IT consulting, coaching, and implementation with a focus on AI
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
Oleg A.
Last position:
Staff Software Engineer at Kpler Germany GmbH
- Delivered a new notifications platform implementation built from scratch to replace existing and upcoming services
- Collaborating with other teams to integrate more domains
Tech stack:
- Data: Scala 3, Apache Kafka, Python, Airflow, Astronomer
- BE-FE: TypeScript, NestJS, Java, Spring Boot, Vue
- Dev-ops: AWS, PostgreSQL, Docker, GitHub Actions, Kubernetes, Helm, ArgoCD
Jeet P.
Last position:
Global SAP Program Manager at Aldi Sued
- Pioneered first enterprise AI-SAP integration at ALDI SÃœD, deploying AI-driven automation within one of retail's largest SAP S/4HANA programs, eliminating 50% of manual pre-cycle validation time and establishing replicable automation framework across 11 countries
- Led end-to-end SAP project lifecycle management for implementations across SAP S/4HANA and Manhattan Systems, supporting 7,300+ ALDI SÃœD locations globally across Europe and Australia
- Served as primary executive liaison to C-level stakeholders across 11 countries for strategic SAP transformation programs
- Orchestrated automation, performance, and volume testing for critical releases, maintaining 99.9% system SLA compliance during peak retail periods
- Managed cross-functional international teams of 15+ specialists, delivering projects 20% faster than industry benchmarks
- Standardized SAP processes across 11 countries as part of one of retail's largest SAP implementations
- Directly managed €2M budget with 98% allocation accuracy across 12 concurrent projects
- Reduced SAP S/4HANA migration costs by 18% through strategic vendor contract renegotiations and optimization
Ottavio B.
Last position:
Semantic Test Framework for LLMs
Jad N.
Last position:
Software Developer at Side Project
- Vram.run: Rust, TypeScript, HF Inference API with 19 providers, 220+ HW configs, and 30+ cloud GPUs. Search a model to see which API providers serve it, which GPUs can run it locally (and how fast), and what cloud rental would cost. Or search your hardware and see what fits. Also includes a Rust CLI.
- Psychotron: JavaScript, Web Audio API, AudioWorklet, Canvas 2D. Front-end for flash fiction audiobook with Web Audio DSP chain featuring pitch-shifting, 12-voice chorus, flanger, 13-band EQ, and convolver reverb. Includes a 2D canvas effect morphing engine and synchronized teleprompter.
- RecentWork: Swift, macOS, FSEvents, launchd. macOS daemon that watches project directories and maintains a flat folder of symlinks to recently modified files. Homebrew installable.
- Mini-llm: Bash, macOS, launchd, Ollama, llama.cpp, MLX, Open WebUI. Single command that turns a Mac Mini into a headless AI server.
- ThatSlop: JavaScript. Chrome/Firefox extension for AI content detection on LinkedIn and Twitter.
- Smux: Bash, tmux. Human-friendly tmux wrapper that is Homebrew installable.
- Learn Rust Course: Rust. Course on Rust’s memory model for C++ programmers, written from experience of transitioning from C++ to Rust at Irreducible.
Meisam G.
Last position:
Senior AI Engineer / Data Scientist at Geeks Ltd (WordUp)
Geeks Ltd is a UK-based technology company; WordUp is its AI-driven language-learning product focused on personalized vocabulary learning and intelligent educational experiences.
- Coordinate AI product delivery across Product, Engineering, Data, Operations, and leadership, translating user needs into scoped initiatives, sequencing work, surfacing blockers, facilitating hand-offs, and communicating progress.
- Own search, recommendation, retrieval, and content-enrichment features end to end, from requirements and architecture through Python/FastAPI implementation, testing, deployment, monitoring, and rapid iteration.
- Developed low-latency retrieval, ranking, and personalization services using AWS, OpenSearch, DynamoDB, embeddings, and reusable APIs, achieving <1s latency, 22% higher engagement, and 12% higher premium conversion.
- Use AI coding assistants for codebase analysis, scaffolding, refactoring, tests, debugging, and documentation while reviewing every output for correctness, architectural fit, security, maintainability, and user value.
- Represent technical work in planning and stakeholder discussions, gather requirements first-hand, challenge priorities constructively, explain delivery trade-offs, and help teammates make outcome-focused decisions.
Muskan V.
Last position:
AI Engineer at Sagas IT Analytics
- Built an AI Research Assistant with RAG, LangChain, LangGraph, and OpenAI LLMs integrated with vector search; cut research time by 30%.
- Designed custom retrieval workflows with LlamaIndex, building a ReAct-style agent for dynamic chunking; improved query accuracy by 18%.
- Researched and optimized embedding strategies, reducing retrieval cost/query by 15%.
- Developed RAG evaluation frameworks using RAGAS and Langsmith with custom datasets; improved coverage by 40%.
- Fine-tuned LLMs (LLaMA 2 on Vertex AI with custom inference containers, dynamic batching, and quantization); reduced inference latency by 25%.
- Integrated AI agents in LangGraph with short-term & long-term memory (Mem0); increased task completion rate by 20%.
- Created schema-aware synthetic data generators; fine-tuned downstream models achieving +12% F1 score.
Shyam Sundar R.
Last position:
GenAI Engineer at Freelance
- Built a hybrid semantic and keyword search and LLM-based requirement extraction from conversational queries, boosting search accuracy by 85%, cutting zero-result searches by 70%, and reducing search time by 60%.
- Deployed a production-ready API with monitoring dashboards over 100K+ products, keeping response times under 2s and reducing customer search-to-purchase time by 40%.
- Technologies: Python, BGE-M3, Qwen2.5, FastAPI, Qdrant, Meilisearch, Docker, Prometheus, vLLM.
Katharina S.
Last position:
AI Engineer
- Designed and implemented end-to-end automated workflows for extracting structured data from semi-structured PDF documents including invoices and medical reports
- Leveraged Optical Character Recognition (OCR) technology and large language models to parse documents and generate validated JSON schemas
- Engineered prompt optimization strategies and rule-based classification hierarchies to enhance parsing accuracy across diverse document layouts
- Established quality assurance framework using evaluation metrics to validate output against ground truth datasets with 96% accuracy
Discover over 15,000 top freelancers
Statistics of experts using Llama
Aggregated from the professional profiles of matched freelancers.
Experience
13 years (Germany: 15 years)
Position duration
2.1 years (Germany: 1.7 years)
Positions per freelancer
9 (Germany: 11)
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Automotive, Education
Certification focus areas
Information Technology, Product Development, Business Intelligence
Bachelor's degree or higher
92% (Germany: 96%)
Master's degree or higher
62% (Germany: 79%)
Doctorate
8% (Germany: 21%)
Certifications per freelancer
1 (Germany: 2)
Most common languages
English, German, French
Speak two or more languages
93% (Germany: 98%)
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 Llama
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.
Llama 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 (93%)
- Automotive (50%)
- Education (43%)
- Professional Services (43%)
- Banking and Finance (36%)
- Healthcare (36%)
- Retail (36%)
- Media and Entertainment (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Llama is
Llama is Meta’s family of open-weight large language models, commonly known as Meta Llama and formerly written as LLaMA. Companies use it to generate and classify text, extract structured information, answer questions and power conversational products. Its downloadable model weights support more control over deployment, data handling and customization than hosted-only services.
What it builds
Llama supports products that need language understanding inside a wider business system. Specialists use it for customer support assistants, internal knowledge search, document processing, content workflows and domain-specific copilots. The right model choice depends on response quality, latency, hardware, context size and the need for local or private inference.
- Retrieval-augmented generation with company content
- Fine-tuned classification and extraction workflows
- Multilingual assistants and enterprise search
- Structured output for operational systems
Ecosystem and tooling
Strong work with Llama covers more than prompt design. Professionals often combine Python, PyTorch, Hugging Face Transformers, vLLM or llama.cpp with vector databases, embedding models and evaluation tools. They may also use quantization, parameter-efficient fine-tuning and containerized inference to fit a model to the available infrastructure.
When companies need specialists
Freelance expertise helps when a proof of concept must become a dependable product, or when an existing language system gives inconsistent answers. Companies may also bring in specialists to select a suitable Llama variant, prepare training data, reduce inference costs, connect private documents or establish evaluation and monitoring practices.
- A prototype lacks repeatable quality
- Sensitive data requires controlled deployment
- Model responses need domain-specific behavior
- Inference performance is difficult to maintain
Working with Berlin teams
Llama projects in Berlin can support technology, media, research, commerce and industrial companies, with collaboration shaped around each team’s security and infrastructure needs. Remote work is often practical for model development, while on-site sessions can help with workshops, data access and product alignment. Clear English is common; German may matter for local users, source material or stakeholder communication.
What strong professionals deliver
A capable Llama specialist explains trade-offs instead of treating the model as a black box. They define evaluation sets, test factuality and safety, document prompts and data flows, and separate retrieval problems from model problems. They also deliver maintainable interfaces, reproducible deployments and a handover that lets the internal team operate the system with confidence.
Frequently asked questions
Need clarity? These are the questions we hear most often about Llama.
Llama is used for text generation, summarization, classification, extraction, question answering and conversational interfaces. Companies also use it in retrieval-augmented generation systems that connect model responses to private documents or structured business data.
Meta Llama gives companies more control over model hosting, customization and data movement than a hosted-only API. The trade-off is greater responsibility for infrastructure, optimization, security, evaluation and ongoing operations.
A strong Llama specialist often works with Python, PyTorch, Hugging Face Transformers, vector databases and embedding models. Experience with vLLM, llama.cpp, quantization, fine-tuning, data pipelines and cloud or private infrastructure is also valuable.
The need depends on the scope. A simple prototype may need prompt, API and evaluation skills, while a production system calls for experience with model selection, data preparation, security, inference performance and monitoring. Ask for evidence of comparable deliverables rather than relying on a generic background.
Llama work is often well suited to remote collaboration from Berlin because model development, testing and documentation are largely digital. On-site workshops can still help when specialists need access to protected data, close product collaboration or direct alignment with German-speaking stakeholders.
LLaMA fine-tuning can help when the desired behavior, format or domain language must become consistent across many inputs. Retrieval is usually better when the main challenge is giving the model current or private facts that change over time.
Look for clear evaluation criteria, representative test data and measured handling of incorrect or unsafe responses. A reliable Llama professional should explain latency, context limits, data privacy, fallback behavior and how the system will be monitored after release.
Meta Llama projects require a review of the applicable model license and any usage conditions before release. A careful specialist checks those terms alongside data rights, third-party components, deployment plans and the client’s intended commercial use.
The average hourly rate of freelancers in Berlin, Germany who have used Llama in their recent projects is 109 €, which corresponds to a daily rate of about 871 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Llama in their recent projects, 92% hold at least a Bachelor's degree, 62% hold at least a Master's degree, and 8% hold a doctorate.
On average, freelancers in Berlin, Germany who have used Llama in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Berlin, Germany who have used Llama in their recent projects are English (100%), German (93%), and French (21%).
The most common industries among freelancers in Berlin, Germany who have used Llama in their recent projects are Information Technology (93%), Automotive (50%), and Education (43%).
The most common business areas among freelancers in Berlin, Germany who have used Llama 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 Llama
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 
Munich Request a free demo
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Philipp Thomaschewski
FRATCH CEO
