Llama Experts in Berlin
matched in minutes from over 15,000 CVsHire experts who fine-tune Meta Llama models, build retrieval-augmented generation systems and deploy reliable inference services. FRATCH matches you precisely with vetted, available freelancers who can work remotely or alongside your Berlin team.
Meet FRATCH Experts in Berlin, who have recently used Llama
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
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.
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.
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
Jeet P.
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 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: 14 years)
Position duration
2 years (Germany: 1.8 years)
Positions per freelancer
9 (Germany: 11)
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Education, Automotive
Certification focus areas
Information Technology, Product Development, Business Intelligence
Bachelor's degree or higher
93% (Germany: 96%)
Master's degree or higher
79% (Germany: 82%)
Doctorate
7% (Germany: 16%)
Certifications per freelancer
2
Most common languages
English, German, French
Speak two or more languages
93% (Germany: 98%)
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 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 29 Oct 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%)
- Education (50%)
- Automotive (43%)
- Retail (43%)
- Banking and Finance (36%)
- Healthcare (36%)
- Professional Services (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 openly available large language models, also known as Meta Llama or LLaMA. Companies use it to generate and classify text, extract information, answer questions and power conversational features. Its model weights and tooling support more control over deployment than many closed AI services.
Products it powers
Llama specialists turn the model into useful business software rather than treating it as a standalone chatbot. Typical deliverables include:
- Internal knowledge assistants with retrieval-augmented generation
- Document extraction and structured data pipelines
- Customer support and multilingual conversation flows
- Content review, summarisation and classification services
- Private inference APIs for product integrations
Ecosystem and tooling
Work with Llama often involves Python, PyTorch, Hugging Face Transformers and model-serving frameworks such as vLLM or llama.cpp. Strong specialists understand tokenisation, prompt design, embeddings, vector databases and evaluation. They may also use quantisation, parameter-efficient fine-tuning and GPU orchestration to control latency and infrastructure needs.
When companies need help
Freelance expertise is useful when a proof of concept must become a dependable product, or when an existing language model gives inconsistent results. Companies also bring in specialists to select a suitable Llama release, prepare private data, design evaluation sets and connect the model to existing systems. In Berlin, remote collaboration is common, while workshops with product, security and data teams may benefit from on-site time.
What strong specialists deliver
A capable Llama professional starts with the business task and defines measurable acceptance criteria before changing prompts or models. They separate retrieval, generation and application logic, protect sensitive data and test responses against realistic edge cases. They can explain trade-offs between model quality, context size, hardware, speed and operating complexity in clear terms.
From prototype to production
Production work includes access controls, prompt and model versioning, observability, fallback behaviour and human review where needed. Specialists should document data sources, licensing considerations and deployment assumptions. They also know when a smaller quantised model, a hosted API or a different open-weight model is a better fit than Llama for the intended workload.
Frequently asked questions
Need clarity? These are the questions we hear most often about Llama.
Llama is used for conversational assistants, document analysis, summarisation, classification, search interfaces and text generation. It can run behind internal tools or customer-facing products when the organisation needs control over data, model behaviour or deployment.
Llama gives companies access to open-weight models that can be self-hosted, adapted and integrated into private environments. Closed APIs may offer simpler operations or strong general performance, so the right choice depends on data controls, customisation needs, hardware and total operating effort.
A strong Llama specialist usually works with Python, PyTorch, Hugging Face, vector databases and retrieval-augmented generation. They should also understand prompt evaluation, fine-tuning, model serving, GPU infrastructure, security and the application stack around the model.
A small proof of concept may need a specialist who can configure an existing model and connect it to representative data. A production system calls for deeper experience with evaluation, fine-tuning, serving, monitoring, access control and failure handling. The required depth depends on risk and integration complexity, not just the model name.
Yes. Llama work is well suited to remote collaboration when repositories, data access, environments and evaluation criteria are organised. Berlin teams may still prefer on-site workshops for sensitive use cases, stakeholder alignment or language-specific requirements.
Ask how the specialist would establish a test set, measure factuality and relevance, protect private data and investigate poor responses. A credible Llama professional can show clear trade-offs and explain how retrieval, prompts, model settings and application logic affect results.
LLaMA was the original name associated with Meta’s model family, while Llama 2 and later releases are successive model generations. Meta Llama is the current family name used across releases, and a specialist should confirm the exact version, licence and deployment requirements before work begins.
Fine-tuning Llama can help it follow a specialised style, format or task pattern consistently. Retrieval is usually better for information that changes often because source documents can be updated without retraining the model. Many reliable systems combine both approaches with structured evaluation.
The average hourly rate of freelancers in Berlin, Germany who have used Llama in their recent projects is 113 €, which corresponds to a daily rate of about 903 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Llama in their recent projects, 93% hold at least a Bachelor's degree, 79% hold at least a Master's degree, and 7% 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 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%), Education (50%), and Automotive (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.
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Philipp Thomaschewski
FRATCH CEO
