Google Vertex AI Experts in Berlin
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Meet FRATCH Experts in Berlin, who have recently used Google Vertex AI
Deepak Mishra
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 Zahid
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.
Viktor Shcherban
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
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.
Apoorv Singh
Last position:
AI Interviewer
- Built an AI Research Assistant with RAG, LangChain, LangGraph, and OpenAI LLMs integrated with vector search.
Kashaf Khan
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 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.
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 (Germany: 1.7 years)
Positions per freelancer
7 (Germany: 9)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Healthcare, Retail
Bachelor's degree or higher
100% (Germany: 94%)
Master's degree or higher
86% (Germany: 81%)
Certifications per freelancer
1 (Germany: 4)
Most common languages
English, German, Ukrainian
Speak two or more languages
71% (Germany: 92%)
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 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What it is
Google Vertex AI is Google Cloud’s managed AI and ML stack. Teams use it to train, tune, deploy, and monitor models in one place. It also supports generative AI work through Gemini, prompt design, grounding, and model evaluation.
Typical work
Experts use Vertex AI for production tasks such as:
- building ML training and prediction workflows
- connecting BigQuery, Cloud Storage, and data pipelines
- creating GenAI apps with prompts, safety checks, and evaluation
- deploying models as online or batch services
- monitoring drift, quality, and cost
Ecosystem fit
Vertex AI sits inside the wider Google Cloud stack. Strong professionals know how to work with BigQuery, Cloud Storage, Pub/Sub, Dataflow, and IAM, plus Python, notebooks, and container-based delivery. They also know when to use AutoML, custom training, or foundation models from the Vertex AI ecosystem.
When to bring in help
Companies usually bring in freelance expertise when a project needs a clean start, a rescue, or a faster path to production. Berlin teams often need support for internal tools, customer-facing AI features, and data-heavy products that must fit existing Google Cloud systems. The right specialist can shorten setup time and avoid costly rework.
What strong specialists do
Good Vertex AI professionals do more than call APIs. They define the use case, choose the right model path, set up evaluation, and make deployment reliable. They write clear prompts, manage versions, understand security and access, and keep the solution practical for the business.
Signals you need one
- you have data in Google Cloud but no working AI flow yet
- your model works in testing but not in production
- you need Gemini-based features with safer outputs
- you must connect AI work to business systems and controls
- your team needs help with Vertex AI, not generic cloud advice
Frequently asked questions
Before you brief your next project: the most common questions about Google Vertex AI.
Google Vertex AI is used to build and run machine learning and generative AI systems on Google Cloud. Teams use it for training, tuning, deployment, prompt-based apps, evaluation, and monitoring. It fits projects that need one managed place for the full model lifecycle.
Vertex AI is the newer Google Cloud product that replaced AI Platform for most modern ML and GenAI work. The old name still appears in documents and in the market, so freelancers should understand both. In practice, most new projects are designed around Vertex AI services and workflows.
Google Vertex AI freelancers are useful when you need focused delivery, a rescue on a stalled project, or short-term expertise for a specific model workflow. They are also helpful when your team knows Google Cloud but needs deeper experience with model evaluation, deployment, or Gemini-based features. That makes sense for Berlin companies that want to move fast without hiring full time.
A strong Vertex AI specialist usually brings Python, Google Cloud, SQL, data pipeline knowledge, and solid model evaluation habits. Experience with BigQuery, Cloud Storage, IAM, and containerized deployments is often important too. For GenAI work, prompt design, grounding, and safety checks matter as well.
Vertex AI work can start simple, but production systems need more than basic API knowledge. Look for someone who has shipped model workflows, handled deployment and monitoring, and can explain trade-offs clearly. If the project touches data governance or customer-facing AI, deeper experience is worth it.
Google Vertex AI is often chosen when a team already works heavily in Google Cloud or wants tight access to BigQuery and Google’s model stack. SageMaker and Azure Machine Learning solve similar problems, but the surrounding cloud services and team habits often decide the best fit. The right freelancer should compare the options in the context of your architecture, not in general terms.
Yes. Vertex AI work is often remote-friendly because most tasks happen in code, cloud consoles, and shared data environments. For Berlin teams, a freelancer can usually collaborate remotely in English, with on-site sessions only when the project needs workshops or stakeholder alignment.
A strong Vertex AI professional shows clear project examples, not vague cloud claims. Look for evidence of shipped solutions, sensible model choices, clean documentation, and a practical approach to evaluation and monitoring. The best experts can explain why they chose a certain path and how they would keep it stable in production.
The average hourly rate of freelancers in Berlin, Germany who have used Google Vertex AI in their recent projects is 91 €, which corresponds to a daily rate of about 731 € 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 86% 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 (71%), and Ukrainian (14%).
The most common industries among freelancers in Berlin, Germany who have used Google Vertex AI in their recent projects are Information Technology (100%), Healthcare (57%), and Retail (43%).
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 (86%).
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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Philipp Thomaschewski
FRATCH CEO
