Llama Experts in Berlin
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Meet FRATCH Experts in Berlin, who have recently used Llama
Sunish Bharathan
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.
Oleg Abrazhaev
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
Hamza Khan
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.
Louis Guitton
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
Jeet Pattanaik
Last position:
Global SAP Program Manager at Aldi Sued
- Pioneered first enterprise AI-SAP integration at ALDI SÜD, 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 SÜD 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 Braun
Last position:
Semantic Test Framework for LLMs
Meisam Ghafarlangroudi
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 Verma
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 Rampalli
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.
Mark Wernsdorfer
Last position:
Independent IT/AI Consultant at Freelance
- IT consulting, coaching, and implementation with a focus on AI
Discover over 15,000 top freelancers
Statistics of experts using Llama
Aggregated from the professional profiles of matched freelancers.
Experience
12 years
Position duration
2.1 years
Positions per freelancer
8
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Automotive, Education
Certification focus areas
Business Intelligence, Information Technology, Product Development
Bachelor's degree or higher
90%
Master's degree or higher
60%
Doctorate
10%
Certifications per freelancer
1
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What Llama does
Llama is a family of large language models from Meta used to build text assistants, search tools, and content workflows. Teams use it for chat, summarization, extraction, classification, and retrieval-augmented generation. It fits products that need strong language handling without building a model from scratch.
Common project types
- Chat assistants for support, sales, or internal knowledge
- RAG systems that answer from company documents
- Content drafting and message rewriting workflows
- Structured data extraction from tickets, PDFs, and notes
- Safety and prompt design for production use
Ecosystem and stack
Strong Llama work sits around the model itself, not just a prompt. Professionals often combine it with Python, PyTorch, Hugging Face, vector databases, and orchestration tools such as LangChain or LlamaIndex. They also know how to run open-weight models through APIs or on managed infrastructure.
When to bring in specialists
Companies usually bring in freelance Llama experts when a prototype needs to become reliable software. That often means prompt tuning, retrieval design, latency work, evaluation, or adapting the model to a domain like legal, finance, media, or customer service. In Berlin, this is common for teams that need English-first delivery but also want clean German output.
What strong professionals deliver
A good specialist does more than connect a model to a chatbot shell. Look for people who can define prompts, build guardrails, test output quality, trace failures, and improve response grounding. They should also understand deployment trade-offs, privacy constraints, and how to keep outputs useful when the source data changes.
How to choose the right fit
- Clear experience with Llama, not only generic LLM work
- Hands-on work with RAG, embeddings, and evaluation
- Ability to explain trade-offs in plain language
- Strong delivery habits around testing and iteration
- Comfort working with product, data, and security teams
Frequently asked questions
Need clarity? These are the questions we hear most often about Llama.
Llama is commonly used for assistants, document search, summarization, classification, and content workflows. It is a good fit when a product needs language understanding and controlled responses, especially with internal knowledge or domain-specific data. Many teams also use it to power RAG systems instead of relying on a raw chat interface.
Meta Llama is the name many people use when they refer to the model family from Meta. In practice, searchers may shorten it to Llama, and they usually mean work around prompts, retrieval, evaluation, and deployment of a large language model. That is broader than simple chatbot setup.
Llama is often chosen when teams want more control over the model stack, deployment path, or customization options. GPT and Claude are strong alternatives, but they are usually weighed differently depending on hosting, governance, and product needs. A good freelancer can explain the trade-offs without pushing one model by default.
A strong Llama specialist usually also works comfortably with Python, embeddings, vector search, and prompt design. Experience with Hugging Face, LangChain, or LlamaIndex is often useful, along with basic evaluation methods and deployment knowledge. The best people can connect model behavior to product outcomes.
You do not need a finished architecture before hiring a Llama expert. Many companies bring in help at the idea stage, once they know the use case and have sample data or a first prototype. The earlier the specialist joins, the easier it is to avoid weak prompts, poor retrieval, and rework later.
Most Llama work can be done remotely because the core tasks are design, testing, and integration. On-site time in Berlin helps when teams need fast workshops, sensitive data review, or close work with product and security stakeholders. Many projects use a mixed setup.
Look for concrete examples of production outcomes, not just model names. A good Llama freelancer can describe how they improved grounding, reduced bad answers, or built evaluation checks around real user flows. Clear thinking about failure modes matters as much as coding skill.
A Llama engagement often delivers a working prototype, prompt and retrieval design, evaluation notes, and a deployment plan. In stronger projects, you also get safety rules, fallback behavior, and documentation for future changes. The exact output depends on whether the goal is a pilot, an internal tool, or a customer-facing feature.
The average hourly rate of freelancers in Berlin, Germany who have used Llama in their recent projects is 105 €, which corresponds to a daily rate of about 838 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Llama in their recent projects, 90% hold at least a Bachelor's degree, 60% hold at least a Master's degree, and 10% hold a doctorate.
On average, freelancers in Berlin, Germany who have used Llama in their recent projects have 12 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 German (100%), English (100%), and French (20%).
The most common industries among freelancers in Berlin, Germany who have used Llama in their recent projects are Information Technology (90%), Automotive (50%), and Education (50%).
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 (90%).
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.
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
