AI Architects in Berlin
matched in minutes from over 15,000 CVs with the power of AI.Deploy production-grade machine learning pipelines, design scalable LLM architectures, and establish robust MLOps practices. Connect with vetted, available freelance AI architects specializing in TensorFlow, PyTorch, and cloud-native AI infrastructure.
Meet FRATCH AI Architects in Berlin
William N.
Last position:
Power BI Solutions Architect/Engineer & AI Consultant at AVERDUNG GmbH
- Redesign of the company’s BI infrastructure: replacement of a fragmented landscape of manually maintained Excel solutions and CSV imports with a centralized Power BI environment using a unified data model as the company-wide single source of truth
- Consolidation of previously isolated reporting logic in a central semantic model – elimination of redundant files, manual data transfers and inconsistent metrics across departments
- Forecasting & Planning: Design and implementation of company-wide liquidity planning in Power BI – from business logic to a fully automated, data-source-driven planning model replacing the previous manual Excel process; enabling rolling forecasts and continuously up-to-date cash flow transparency for management
- Optimization of existing Power BI dashboards in terms of performance, structure and analytical value using an AI-native approach
- Analysis and improvement of the data model, including data quality analyses, data cleansing and consistent modeling using Star Schema, DAX and Power Query
- Incident & Anomaly Analysis: Identification, investigation and explanation of data anomalies, including root-cause analysis and specific recommendations for action
- AI Solution Architecture: Connecting Business Central and Power BI to LangDock via MCP (Model Context Protocol) for AI-supported data usage
- Development of a historical data layer as a basis for trend and time-series analyses
- AI-supported automation: Design and development of AI skills, agents, loops and processes for the automated analysis and interpretation of reports
- Automated reporting workflow: Setup of scheduled, automated email delivery of AI-generated analyses and recommendations to stakeholders
- Gathering and documentation of business requirements and coordination with business departments and IT as part of requirements engineering / product owner activities
- Breaking down overall requirements into clearly defined work packages and tasks
- Definition, prioritization and management of milestones throughout the entire project lifecycle
Tools: POWER BI, M365, Copilot Studio, MIRO, Microsoft Business Central, Microsoft Fabric, Claude AI, ChatGPT, LangDock, MS VS Code
Tommy S.
Last position:
Process Manager · Order-to-Cash & Automation at EWE Tel GmbH
- Root cause analysis of complex business, technical, and data-related errors in PowerCloud across process, booking, and system boundaries.
- Data-driven management of payments and receivables; contributed to reducing historical receivables from over 100 Mio. EUR to under 25 Mio. EUR.
- Identification of automation and straight-through processing potential at the interface between business departments, IT, and external service providers.
Marvin M.
Last position:
Co-founder & CTO · Freelance Software Engineer (AI & SaaS) at Self-employed
- Self-employed · Berlin, Germany
- Co-founded the company and own the entire technical side: product architecture, backend, frontend, infrastructure and operations.
- Designed and built the Automated Booking System (ABS) as well as the core platform and payment logic.
- Development of AI-powered SaaS products, from architecture through backend and frontend to production operation.
- Technical consulting on product architecture, data protection and permissible automation.
- Building, running and monetising my own API products for AI agents, each available as a REST interface and as an MCP server.
- Full ownership of architecture, infrastructure, billing, legal texts and go-to-market.
Vito B.
Last position:
AI Architect & Engineer (Founder) at Arcate
Impact: Arcate turns scattered customer signals into ranked product decisions for B2B product teams. Every initiative is prioritized by revenue at risk. Every decision is traceable from customer quote to board slide. Built solo, deployed in production. Model validated at Kendall's tau = 0.924 vs. senior PM judgment across 60 simulation runs.
Skills: Artificial Intelligence, AI Agent, Large Language Model (LLM), RAG, Model Context Protocol (MCP), Agentic Workflows, Supabase, TypeScript, Deno, PHP, Stripe, PostgreSQL, Product Strategy, Positioning, GTM
Capabilities:
Signal Ingestion: Slack, Intercom, Gong, Salesforce, HubSpot. Signals classified by business severity to prioritize revenue-risk decisions.
Revenue Scoring: Fermi Leverage model. Initiatives ranked by customer ARR at risk, signal strength, and multi-account confirmation.
Roadmap Intelligence: Every bet traceable from raw customer signal to scored, board-ready decision.
Built:
MCP Server (v0.10.0): 12 tools, JSON-RPC 2.0, Supabase Edge Functions, SHA-256 API key authentication.
Scoring engine: Log-scaled ARR weighting, sqrt-dampened signal strength, multi-account signal confirmation.
Agentic Workflows: 18 automated pipelines covering release, provisioning, design QA, guard QA, and signal ingestion via Slack agents.
AI Skills: 7 codified skills including CEO Prioritizer, Design System Enforcer, MCP QA, Simulation Runner.
Automated QA: Browser-based screenshot validation of every screen against design tokens on every build.
Full SaaS: Auth, billing, media pipeline, design system. Deployed solo in production.
Stack: Supabase (Auth, DB, Edge Functions, Realtime), Stripe, Cloudinary, PHP, TypeScript, Deno
Nune I.
Last position:
Fractional CTO at OpsWorker
OpsWorker turns Kubernetes alerts into root-cause analyses, on top of the monitoring a team already runs. I lead the technical side: the agent architecture, the AWS infrastructure it runs on (fully inside EU regions), and the engineering decisions behind it, read-only in the cluster by default, human in the loop for judgment. The stack underneath: Amazon Bedrock and Bedrock AgentCore, agents built with the Strands Agents SDK, the Claude and OpenAI APIs, and the Kubernetes API.
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
Ottavio B.
Last position:
Semantic Test Framework for LLMs
Umar M.
Last position:
Senior AI Architect & Engineer at Freelancer / Self-Employed
- Consulting, design and architecture of SaaS platforms with a focus on automation, data analysis and cloud deployment
- Definition of the target architecture and management of the entire development lifecycle from implementation to production operation, including stakeholder alignment
- Design, architecture and implementation of a multi-tenant SaaS platform
- Development of scalable data and machine learning pipelines (Batch & Streaming) for order and business data
- Development of AI models for data analysis (KPI calculations, forecasts) and integration into data pipelines
- AI-supported processing of customer inquiries (delivery status, invoice, cancellation, complaint) to automate customer service
- Development of APIs, microservices and dashboards with Python for data-driven applications
- Cloud deployment on AWS and infrastructure-as-code automation with Terraform; containerization with Docker and Kubernetes
- Development of CI/CD pipelines for automated deployment with GitLab CI and governance of deployment processes
- Implementation of monitoring dashboards with Grafana to monitor services and ML pipelines
- Implementation of security and compliance requirements (GDPR-compliant data handling, logging), including identity and access management and role-based access control
Tino T.
Last position:
Director Technology at Forte Digital Germany
- Leading 20+ staff in development, site reliability engineering, and architecture.
- Leading the group-wide agentic AI initiative (Norway, Poland, Germany).
- Hands-on solution architect and AI consultant for over 50% of my working time on client projects in the publishing sector – from local publishers to international corporations.
- Strategic consulting and technical implementation of AI workflow platforms (n8n, Workato).
- Developing prototypes for traditional, AI-based, and agentic AI workflows.
Mohamed G.
Last position:
Lead / Principal Cloud, AI & Security Architect at Freelancer / CC Conceptualise GmbH
Projects:
Project: RWE – Development of a company-wide Zero Trust cybersecurity architecture (CITADEL) Role: Senior Enterprise Cybersecurity Architect / Zero Trust Architect Company: RWE AG Description: Concept and implementation of the strategic CITADEL cybersecurity target architecture at RWE, based on the Zero Trust architecture principle and aligned with regulatory requirements such as NIS2, ISO 27001 and company-wide security governance policies. The goal was to build a measurable, auditable and scalable security architecture with a strong focus on Identity Governance, compliance transparency and operational manageability. Responsibilities & Achievements:
- Zero Trust architecture design: Developed a company-wide Zero Trust reference architecture (Identity, Device, Network, Application, Data) including trust zones, control points and enforcement mechanisms according to NIS2.
- Identity & Access Governance (IGA): Designed and introduced IGA governance structures including role models, recertification processes, segregation of duties (SoD) and lifecycle management for identities and access.
- Security governance & KPIs: Defined and implemented security KPIs and metrics to manage Zero Trust maturity, identity risks and compliance at the management level.
- Compliance & reporting: Built standardized compliance reports and dashboards to support internal audits, external assessments and regulatory evidence (e.g. NIS2).
- Architecture & stakeholder alignment: Worked closely with Enterprise Architecture, IT operations and business units to integrate the CITADEL architecture into existing IT and security landscapes.
- Strategic security consulting: Advised programs and projects on Zero Trust compliance, identity centricity and regulatory requirements in the energy and critical infrastructure (KRITIS) environment. Technologies & Methods: Zero Trust Architecture, NIS2, Identity Governance & Administration (IGA), IAM, RBAC, SoD, Entra ID, SailPoint, Zscaler, Terraform / IaC, Policy as Code, security KPIs, compliance reporting, NIST 2.0, ISO 27001, Enterprise Security Architecture, governance frameworks, risk & control management
Project: Scalable AI Workbench Platform on Microsoft Azure Role: Cloud Architect & Engineer Company: Siemens Energy Description: Design, development and operation of a secure, modular cloud infrastructure to support Data Science, Machine Learning and AI applications for various engineering teams at Siemens Energy. Responsibilities & Achievements:
- Cloud architecture: Designed and implemented an Infrastructure-as-Code solution (Terraform) for automated provisioning of Azure resources (Resource Groups, Storage Accounts, Cosmos DB, Application Insights, networking, PostgreSQL Flexible Server, Azure Container Apps, Azure Container Registry).
- Developer portal: Used Backstage with custom frontend and backend plugins (Node.js, TypeScript, React.js, PostgreSQL, Container Apps) to enable self-service and empower developers, data scientists and AI/ML engineers.
- Role-based access control: Implemented Azure RBAC to grant targeted access (e.g. Storage Blob Data Contributor, Reader) to engineering groups (e.g. AI Engineers) for relevant resources.
- Data platform engineering: Built and configured a multi-layered storage landscape (Raw, Curated, Vector data), including automated container creation and access control for advanced analytics and AI workloads.
- DevOps integration: Integrated with Azure DevOps for CI/CD pipelines to automate deployment, monitoring and compliance.
- Security & compliance: Implemented Private Endpoints, network policies and Managed Identities to ensure data protection and regulatory compliance.
- Collaboration: Worked closely with cross-functional teams to align the cloud infrastructure with business and technical requirements and drive digital transformation at Siemens Energy. Technologies: Azure, Terraform, Azure DevOps, Cosmos DB, Application Insights, Azure Storage, Private Endpoints, Azure Synapse, Azure Machine Learning, Azure Entra ID, RBAC, Backstage, Node.js, React.js, PostgreSQL, Python (automation), Git
Discover over 15,000 top freelancers
AI Architects statistics
Aggregated from the professional profiles of matched freelancers.
Experience
14 years (Germany: 19 years)
Position duration
2.6 years (Germany: 2.1 years)
Positions per freelancer
9 (Germany: 13)
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Energy, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Human Resources
Bachelor's degree or higher
90% (Germany: 92%)
Master's degree or higher
60% (Germany: 58%)
Certifications per freelancer
3 (Germany: 4)
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this role 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 for AI Architects in Berlin
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.
AI Architects 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%)
- Energy (50%)
- Professional Services (50%)
- Manufacturing (40%)
- Automotive (30%)
- Education (30%)
- Banking and Finance (30%)
- Retail (30%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the role
Designing Enterprise AI Infrastructure
Freelance AI architects bridge the gap between theoretical machine learning models and robust enterprise software systems. They design the blueprint for scalable, secure, and cost-effective AI systems that integrate seamlessly with existing IT landscapes. In Berlin, where tech companies and traditional industries converge, these specialists help businesses transition from experimental prototypes to production-ready AI applications that comply with strict European data privacy standards.
Core Expertise and Technical Toolkits
A professional in this field possesses deep knowledge across software engineering, data pipeline design, and machine learning operations. They evaluate and select the right technologies for training and deploying models at scale.
- Designing end-to-end machine learning pipelines and MLOps workflows.
- Architecting large language model integrations and retrieval-augmented generation systems.
- Selecting cloud-native AI services across AWS, Google Cloud, and Microsoft Azure.
- Structuring data ingestion frameworks for high-volume real-time processing.
- Ensuring compliance with European data sovereignty and governance guidelines.
When to Bring in a Freelance Expert
Companies hire freelance architects when they face complex scaling bottlenecks, lack specialized internal engineering leadership, or need to kickstart a high-priority AI initiative quickly. These professionals provide immediate, objective technical leadership without the long onboarding cycles of permanent hires. They are frequently engaged to audit existing infrastructure, define concrete technology roadmaps, and mentor junior development teams during critical growth phases.
Collaboration in the Berlin Tech Ecosystem
Berlin has established itself as a leading European hub for artificial intelligence, demanding high agility and cross-functional communication. Freelancers in this region typically operate in hybrid models, combining remote deep-work periods with on-site design workshops in the city. Their ability to communicate complex system designs to both software engineers and executive business leaders ensures project alignment and successful deployment.
Frequently asked questions
Quick answers to the questions that come up most around AI Architects.
An AI architect designs the high-level system structure needed to deploy, scale, and monitor machine learning models in production environments. They ensure that data pipelines, cloud infrastructure, and software applications work together reliably and securely.
While a data scientist focuses on building, training, and tuning mathematical models, an AI systems architect builds the infrastructure that allows these models to run efficiently at scale. The architect handles integration, deployment pipelines, resource allocation, and system reliability rather than model creation.
Hiring a freelance machine learning architect allows your team to access senior-level strategic guidance immediately for specific phases, such as system design or cloud migration. This avoids the lengthy recruitment cycles of the competitive tech market and provides flexible, specialized expertise exactly when your project needs it.
A professional artificial intelligence architect works with modern MLOps tools like Kubeflow, MLflow, and Triton Inference Server, alongside deep learning frameworks like PyTorch and TensorFlow. They also utilize containerization technologies like Docker and Kubernetes, and cloud-native AI platforms on AWS, Google Cloud, or Azure.
Most freelance AI system designers in the region utilize a hybrid model, combining remote work for engineering tasks with on-site workshops in Berlin for architecture planning and team alignment. This approach fits well with the local startup ecosystem and established corporate hubs alike.
You should evaluate a freelance lead AI engineer by their portfolio of successfully deployed production systems and their understanding of scalable data engineering. Look for strong communication skills and practical experience handling data privacy regulations, such as GDPR compliance.
Yes, a specialist generative AI architect can design the pipelines required for fine-tuning open-source models, setting up retrieval-augmented generation databases, and managing API costs. They ensure your LLM applications are secure, performant, and correctly integrated into your product ecosystem.
Because Berlin is a highly international tech hub, most freelance AI engineering consultants operate entirely in English. However, having a professional who also speaks German can be highly beneficial for projects in highly regulated sectors or when coordinating with local public institutions.
The average hourly rate for AI Architects in Berlin is 118 €, which corresponds to a daily rate of about 947 € based on an 8-hour working day.
Of the freelancers working as AI Architects in Berlin, 90% hold at least a Bachelor's degree and 60% hold at least a Master's degree.
On average, freelancers working as AI Architects in Berlin have 14 years of professional experience, with a single engagement typically lasting around 2.6 years.
The most common languages among freelancers working as AI Architects in Berlin are German (100%), English (100%), and French (40%).
The most common industries among freelancers working as AI Architects in Berlin are Information Technology (100%), Energy (50%), and Professional Services (50%).
The most common business areas among freelancers working as AI Architects in Berlin are Information Technology (100%), Product Development (100%), and Project Management (70%).
FRATCH AI Architects main locations
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
