Google Vertex AI Experts in Berlin
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Meet FRATCH Experts in Berlin, who have recently used Google Vertex AI
Stefan O.
Last position:
Founder at ProtocolEngine.io
Evidence-led health intelligence platform turning published research into personal health protocols. It scores 430 habits, foods, and supplements against the studies behind them, and moves the score when the evidence moves. Built solo.
- Built the daily ingestion pipeline across PubMed, bioRxiv, and medRxiv: 43,000+ papers from 3,400+ journals processed into 230,000+ typed evidence claims, each one traceable back to the study it came from.
- Designed the six-factor evidence scoring model and the public changelog behind it, so no recommendation ever appears without the papers underneath it. 23,000+ grade changes recorded and explained to date.
- Shipped an entity information model connecting every intervention to its mechanisms, biomarkers, and outcomes: 118 biomarkers with region-specific reference ranges, 77 mechanisms, 32 graded outcomes.
- Built the personalisation layer: blood panel ingestion that reads lab PDFs with a vision model and corrects results for draw time against the user's wake anchor, plus Oura, WHOOP, and Withings integration for daily readiness context.
- Operate eleven specialised review agents over the corpus and codebase, covering paper curation, retrieval quality, health-claim compliance across EU and US regimes, and security.
- Shipped the Evidence Assistant, a RAG assistant that answers from the claim database and cites the underlying papers, plus a B2B practitioner tier, an Expo React Native app, and localisation across 3 languages and 7 markets.
Stack: Next.js 16, TypeScript, Supabase, pgvector, Anthropic Claude, Vercel, DeepInfra.
Deepak M.
Last position:
Lead ML Platform Engineer at Billie GmbH
- Mentor team of 6 ML platform engineers through weekly 1:1s, technical design reviews, and best practices, improving team velocity by 35% through structured sprint planning and skill development programs
- Define 2025–2026 ML platform roadmap in collaboration with Data Science, Cloud Engineering, and Product teams, prioritizing automated model governance, cost attribution systems, and multi-environment deployment strategies
- Partner with Data Science, SRE, and Product stakeholders to align ML platform capabilities with business objectives, reducing data scientist deployment friction by 60% through self-service platforms
- Architect and deliver production-grade MLOps platform supporting 50+ models in production with automated promotion pipelines, versioning, and rollback capabilities, achieving 99.5% platform uptime SLA
- Design distributed ML pipeline architecture using Metaflow and Argo Workflows (Vertex Pipelines-compatible), reducing model training time by 30% and deployment cycles from 2 weeks to 3 days through full CI/CD automation
- Build containerized ML services on Kubernetes with auto-scaling policies, resource quotas, and multi-tenancy isolation, optimizing infrastructure costs by $180K annually (25% reduction)
- Implement monitoring, alerting, and performance tracking using Prometheus, Grafana, and custom instrumentation, reducing model debugging time by 50% and establishing model performance SLOs
- Lead development of RAG-based document intelligence platform using LangChain, LangGraph, and vector databases, implementing agentic AI workflows for automated financial document processing
- Implement Infrastructure-as-Code using Terraform for reproducible environment provisioning and GitOps workflows, reducing infrastructure drift incidents by 80%
- Design role-based access control for ML platform, implement model lineage tracking, and establish audit trails for regulatory compliance aligned with enterprise IAM best practices
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.
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.
Viktor S.
Last position:
AI Engineer (Freelance) at Empion
Enterprise AI content categorization and AI-powered web research.
- Built multi-LLM evaluation framework with annotated data
- Iterated LLM error rates based on annotated datasets
- Implemented AI-powered web research pipeline Stack: LLM, evals, OpenRouter, Python, Node.js, TypeScript, React
Apoorv S.
Last position:
AI Interviewer
- Built an AI Research Assistant with RAG, LangChain, LangGraph, and OpenAI LLMs integrated with vector search.
Kashaf K.
Last position:
AI Consultant / Expert at Siemens Mobility
- Evaluated 45+ AI use cases and developed a prioritization framework for Siemens’ internal AI roadmap.
- Tested internal tools like DRIM, SiemensGPT, Microsoft Copilot; presented evaluation outcomes to stakeholders.
- Identified performance gaps and improved tool adoption by 65%.
- Supported AI knowledge-sharing initiatives, led tool onboarding sessions, and improved team AI literacy.
- Collaborated with engineering, procurement, and digital teams on tool feedback and strategy alignment.
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.
Discover over 15,000 top freelancers
Statistics of experts using Google Vertex AI
Aggregated from the professional profiles of matched freelancers.
Experience
10 years (Germany: 11 years)
Position duration
1.6 years
Positions per freelancer
7 (Germany: 10)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Healthcare, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100% (Germany: 95%)
Master's degree or higher
75% (Germany: 77%)
Certifications per freelancer
2 (Germany: 4)
Most common languages
English, German, Spanish
Speak two or more languages
75% (Germany: 93%)
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 Google Vertex AI
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.
Google Vertex AI 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 (63%)
- Banking and Finance (38%)
- Retail (38%)
- Automotive (25%)
- Media and Entertainment (25%)
- Professional Services (25%)
- Government and Administration (25%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Vertex AI does
Google Vertex AI is a managed Google Cloud platform for building, tuning, deploying and monitoring machine learning and generative AI applications. It brings model development, data preparation, prediction endpoints and operational controls into one environment. Teams use it for language, vision, recommendation and forecasting solutions.
Models and applications
Vertex AI supports foundation models through Model Garden, including Gemini, as well as custom models built with popular machine learning frameworks. Specialists create retrieval-augmented generation systems, document processing workflows, conversational applications and predictive services. They connect models to enterprise data while managing prompts, evaluation and responsible use.
Ecosystem and tooling
The platform works with BigQuery, Cloud Storage, Dataflow, Pub/Sub and Google Kubernetes Engine. Its tooling includes Vertex AI Workbench, pipelines, feature management, model registry, endpoints and monitoring. Strong expertise also covers Python, TensorFlow, PyTorch, APIs, vector search, IAM and infrastructure automation.
When companies need specialists
- A proof of concept must become a secure production service
- Existing data and business systems need an AI integration
- Models require tuning, evaluation or controlled deployment
- GenAI features need grounding, observability and cost control
Companies often bring in freelance professionals when internal teams lack focused Google Cloud or machine learning capacity. In Berlin, remote collaboration is common, while workshops and stakeholder sessions may benefit from local availability and clear English or German communication.
What strong professionals deliver
Experienced Vertex AI specialists clarify the business objective before selecting a model or architecture. They establish reproducible data and deployment workflows, define evaluation criteria and protect sensitive information through appropriate access controls. Their deliverables may include a tested prototype, production API, pipeline, monitoring setup, technical documentation and handover plan.
Choosing the right fit
Look for practical evidence across the relevant layer: generative AI, predictive modeling, data engineering, cloud security or MLOps. Ask how the professional handles unreliable model output, changing model versions, latency, access permissions and rollback plans. The best fit explains trade-offs clearly and leaves behind systems that teams can operate, improve and audit.
Frequently asked questions
Before you brief your next project: the most common questions about Google Vertex AI.
Google Vertex AI is used to build, train, deploy and monitor machine learning and generative AI applications. Common projects include document extraction, search with grounded answers, forecasting, recommendations, image analysis and conversational interfaces.
Google Vertex AI is often chosen when a company already relies on Google Cloud, BigQuery or Gemini models. SageMaker and Azure Machine Learning offer comparable lifecycle capabilities, so the right choice depends on cloud alignment, available skills, data location, model access and integration requirements.
Google Vertex AI work commonly overlaps with Python, SQL, BigQuery, Cloud Storage, IAM, Docker, Kubernetes and infrastructure automation. For generative AI projects, useful adjacent skills include prompt design, retrieval systems, vector databases, evaluation and application security.
Google Vertex AI projects need a professional who matches the delivery stage, not simply someone familiar with the product name. A prototype may need model and application skills, while production work also requires data quality controls, secure deployment, monitoring, testing and reliable handover.
Google Vertex AI projects are well suited to remote collaboration because development, cloud environments and reviews are online. Berlin-based teams may still prefer occasional on-site workshops, and agreeing on English or German communication expectations early helps keep technical and business discussions clear.
Google Vertex AI quality is best assessed through concrete examples of shipped systems, not certificates alone. Ask how the professional measured model performance, protected data, handled failure cases, controlled access and monitored the service after launch.
Google Vertex AI can support private-data use cases through retrieval, controlled data access and managed model services. A suitable specialist should explain grounding, permission boundaries, retention considerations, evaluation and safeguards against exposing confidential information.
Google Vertex AI brought together capabilities that were previously associated with Google Cloud AI Platform and related services. A professional familiar with both naming conventions can usually assess older pipelines and plan a practical migration rather than treating the existing setup as entirely new.
The average hourly rate of freelancers in Berlin, Germany who have used Google Vertex AI in their recent projects is 107 €, which corresponds to a daily rate of about 852 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Google Vertex AI in their recent projects, 100% hold at least a Bachelor's degree and 75% hold at least a Master's degree.
On average, freelancers in Berlin, Germany who have used Google Vertex AI in their recent projects have 10 years of professional experience, with a single engagement typically lasting around 1.6 years.
The most common languages among freelancers in Berlin, Germany who have used Google Vertex AI in their recent projects are English (100%), German (75%), and Spanish (13%).
The most common industries among freelancers in Berlin, Germany who have used Google Vertex AI in their recent projects are Information Technology (100%), Healthcare (63%), and Banking and Finance (38%).
The most common business areas among freelancers in Berlin, Germany who have used Google Vertex AI in their recent projects are Information Technology (100%), Product Development (100%), and Business Intelligence (88%).
Main locations of FRATCH Experts, who have recently used Google Vertex AI
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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