Spring AI Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Spring AI
Fred Hauschel
Last position:
Software Architect and Developer at Personal project
A recurring problem in my own AI-supported projects: requirements analysis, use cases, and architecture decisions can be created quickly with AI support, but they remain hard to trace and scattered across Markdown files – knowledge is lost as soon as it is no longer in the context window. arknet turns requirements engineering and architecture knowledge into structured, verifiable data instead of plain text: requirements, use cases, and architecture decisions as a continuously linked knowledge graph, traceable from the requirement to the architecture decision – queryable for both people and AI agents alike. Technically based on RDF/OWL and its own MCP server.
Result: MCP daemon running, Docker image automatically published on GHCR, nine hexagonal modules, eleven ADRs (including an open-core licensing model). Requirements engineering and ubiquitous language hexagon active. Publicly available since 07/2026 as a Community Edition under Apache-2.0 (github.com/kogn-io/arknet), together with the Claude Code plugin and the GHCR image; open-core model.
Label: Java, Maven, RDF, RDF4J, OWL, SPARQL, Model Context Protocol, Spring AI, Docker, GitHub, Git, Claude Code, Obsidian, DDD, Hexagonal Architecture, ArchUnit, JUnit, AssertJ
Ali Aminian
Last position:
Platform Engineer & Software Architect at Yatta GmbH
- Architected the Yatta Integration Layer – a config-driven integration platform on Java 25, Spring Boot 4 (WebFlux), Temporal, gRPC and Kafka, enabling new third-party integrations (e.g. AVS fulfillment) via declarative JSON configs with zero code changes.
- Designed and implemented Tink integration with 0Auth IBAN verification to enhance fraud prevention and account validation workflows with Adyen payByBank.
- Architected and implemented an OpenFGA-based authorization model for centralized management of users, groups, and fine-grained access control in the vendor portal.
- Architected and led delivery of the Yatta API Gateway platform using GraphQL Federation, providing a unified enterprise API layer across distributed microservices with centralized authentication, authorization and request orchestration.
- Replaced NGINX + NLB with Istio service mesh and AWS ALB; rolled out WAF, OAuth (Cognito), IP whitelisting and RBAC across environments.
- Migrated CDC from Confluent Cloud connectors to a self-hosted Kafka Connect + Debezium stack, reducing operational cost by ~80% across multiple environments.
- Implemented the Transactional Outbox pattern with Debezium for reliable, exactly-once event publishing to Kafka with Avro and Schema Registry.
- Migrated dunning/payment-recovery workflows from Airflow to Temporal, achieving 99.9% reliability for settlement handling.
- Optimised Apache Airflow with deferrable sensors to handle 1000+ concurrent DAG runs without scaling the worker pool.
- Refactored a monolithic Terraform codebase into 3 modular projects, cutting deployment time by ~45%.
- Stood up full observability with OpenTelemetry, Tempo, Prometheus and Loki; automated dev/staging/prod with ArgoCD, Image Updater and Helm.
- Collaborated with product, operations and engineering stakeholders to define scalable platform architecture and integration standards aligned with long-term business and operational goals.
Patrick Waldschmitt
Last position:
AI Software Engineer at IppenMedia
- Analysis
- Consulting
- Software design
- Development
- Automation
- Testing
- Deployment
- Architecture, development and deployment of various proof-of-concept applications around the integration of current AI interfaces including conversational, realtime voice, images and videos
- Developed best practices for working with agentic systems and AI in practice
- Created code templates
Robert Di Marco
Last position:
Software Architect at FinTech / Wealth Management
- Analysis of existing microservice structures and integration into the event-driven architecture
- Design and implementation of prototypes
- Introduction of structured workflows and embedding in a solid testing strategy
- Team: small specialized team (3-5 people) from analysis to implementation readiness
- Documentation of architectural decisions and definition of interfaces
- Integration of the event-driven pipeline into the existing landscape
- Technologies: Kafka, Docker, Microservices, Event-Driven Architecture, Java, Liquibase
Neha Khare
Last position:
Team Lead | Senior Java Developer at Capgemini
- Tech Stack: Java 17, Spring Boot, Microservices, REST, GraphQL, Spring AI, Jenkins, Docker, Git/Bitbucket, JUnit, Splunk
- Led a team of 6 to deliver policy, claims, and onboarding modules serving 50k+ users, maintaining 99.9% uptime
- Cut release cycle time by 35–40% by automating CI/CD with Jenkins and Docker
- Implemented trunk-based branching in Git/Bitbucket
- Integrated 10+ REST APIs and streamlined customer workflows, boosting process automation by 40%
- Increased system stability with proactive Splunk alerting and runbooks, reducing outages by ~25%
- Driven quality with JUnit tests (92% coverage) and code reviews; enforced standards with static checks such as SonarQube
Satya Vulise
Last position:
Lead Developer at Allane Mobility Group
- Led development activities for enterprise applications, managing design, planning, and delivery to meet organizational goals.
- Constructed and deployed microservices using Java/JavaEE, Kotlin, Spring Boot, Kafka incorporating synchronous and asynchronous communication, achieving a 95% on-time delivery rate.
- Developed microservices in Golang utilizing frameworks such as Gin, GORM, and Viper for high-performance applications.
- Maintained RESTful and GraphQL APIs, enabling seamless integration with enterprise applications.
- Leveraged gRPC for secure, efficient service-to-service communication in microservices, reducing latency.
- Used SQL databases (MySQL, PostgreSQL) and NoSQL databases (MongoDB, Redis).
- Integrated publisher-subscriber systems and message queue architectures (SQS, Kafka).
- Configured secure authentication and authorization systems (OAuth 2.0, OpenID Connect) using AWS Cognito and Spring Security.
- Architected scalable patterns like API Gateway, Circuit Breaker, Saga, CQRS, and Event Sourcing to enhance reliability and performance.
- Built middleware solutions integrating complex APIs and third-party services for seamless system interactions.
- Achieved cloud-native architectures with AWS services, including S3, EC2, Lambda, API Gateway, RDS, DynamoDB, SNS, SQS, EKS, ECR, and ECS.
- Delivered a centralized CI/CD pipeline, reducing deployment time by 80% through automation and standardization.
- Integrated observability tools such as Prometheus, Grafana, Datadog, and CloudWatch, improving monitoring and troubleshooting capabilities.
- Automated IaC provisioning with Terraform, ensuring consistent and scalable environments across development, testing, and production.
- Enhanced logging and visualization using the ELK stack (Elasticsearch, Logstash, Kibana).
- Optimized release processes, ensuring efficient and error-free deployments, resulting in a 30% reduction in production bugs.
- Lifted services to the cloud, transitioning legacy systems to a cloud environment to improve scalability and performance.
- Automated infrastructure tasks with Python, streamlining workflows such as S3 file uploads and SQS event handling.
- Crafted Python scripts to test AWS services locally using LocalStack, achieving 98% accuracy.
- Designed and architected large-scale, scalable enterprise applications, performing end-to-end, unit, and integration testing, reducing production bugs by 25%.
- Designed and developed web applications using Angular, HTML, CSS, and JavaScript.
- Integrated advanced security measures into the DevSecOps pipeline, including SAST with SonarQube, DAST using OWASP ZAP, vulnerability scanning with Snyk, container image scanning via Trivy.
- Mentored 5+ junior developers in Java, Kotlin, and microservices, boosting team productivity by 20% within six months.
- Facilitated workshops on modern architecture, DevOps, and cloud integration practices, enhancing team proficiency.
Marcel Seifert
Last position:
Lead Developer / Software Architect at Rezeptprüfstelle Duderstadt GmbH
Responsible for the redevelopment of a billing and validation software for prescriptions to fully check and analyze e-prescriptions for correctness (content, billing)
System consists of multiple contexts running as services (Docker containers):
Checking and processing data deliveries via FTP and email
Management of invoicing, clearings, deductions and offsets
Management and execution of validation rules and test sets
Analytics based on Metabase
Developer Stack: Kotlin, Vue 3 / Vuetify 3, ANTLR, Spring Boot 3, REST API, Gradle, Docker, GitLab, PostgreSQL, Kafka, Keycloak, Scrum, Grafana, Loki, Testcontainers, Prometheus
Gulam Noxboundy
Last position:
Full-Stack Developer at .Attendo
Developed Skalman’s Food & Sleep Clock web app with a digital assistant that reminds users about medication intake, meals, movement breaks, and other daily routines.
Implemented personalized meal suggestions and simple recipes with support for allergies and special diets, integrating AI to generate tailored meal ideas.
Built Flight Booking App using React and Axios for the front end, Spring Boot with JPA and MySQL for the back end, and implemented user authentication, form validation, and booking logic.
Designed and built a chatbot that accepts natural language queries and generates intelligent responses using OpenAI’s language model.
Developed MarketPlace_AP with React and Axios on the front end, Spring Boot with JPA and MySQL on the back end, enabling users to discover services or products, with authentication and form validation.
Created a Meeting Calendar page allowing users to schedule, view, and manage meetings using React, Axios, Spring Boot, JPA, MySQL, and authentication with validation.
Created a Todo List Manager: built a REST API with Node.js, Express, and MongoDB; developed a React front end featuring status toggling and filtering; deployed on Heroku and integrated with GitHub for CI/CD.
Discover over 15,000 top freelancers
Statistics of experts using Spring AI
Aggregated from the professional profiles of matched freelancers.
Experience
19 years
Position duration
1.8 years
Positions per freelancer
12
Top business areas
Information Technology, Product Development, Operations
Top industries
Information Technology, Banking and Finance, Automotive
Certification focus areas
Information Technology, Product Development, Human Resources
Bachelor's degree or higher
100%
Master's degree or higher
60%
Certifications per freelancer
4
Most common languages
German, English, Bangla
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 Germany 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 Germany using Spring 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
Spring AI basics
Spring AI brings generative AI features into the Spring ecosystem. It helps teams connect application code to model providers, prompts, embeddings, and vector stores without leaving the patterns they already use in Spring Boot. It is a fit for chat, search, summarization, and assistant-style features inside existing business software.
What it is used for
- Chat features in internal tools and customer portals
- Retrieval-augmented generation with company documents
- Prompt-driven workflows and content assistance
- Model routing across providers and use cases
Strong specialists know where to keep logic in the service layer and where to delegate to a model call.
Ecosystem and tools
Spring AI works with the wider Spring stack, including Spring Boot, Spring Web, and data access layers. It is often paired with vector databases, message-driven services, observability tools, and secure secret handling. Good professionals also understand prompt design, token limits, and how to keep model responses stable.
When companies bring in help
Companies usually need outside experts when they want to add AI to an existing Java platform without rewriting it. That is common in product teams, enterprise portals, and data-heavy systems in Germany where Spring is already the standard base. Freelancers are also useful for spikes, proof of concept work, and hard integration tasks.
What strong specialists deliver
- Clean integration with existing Spring services
- Reliable prompt and response handling
- Search over internal knowledge sources
- Guardrails, logging, and fallback flows
- Guidance on provider choice and architecture
The best experts write code that is maintainable, testable, and easy for the permanent team to support.
Signs you need Spring AI expertise
If a team is stuck between model providers, has weak retrieval results, or needs to move from demo to production, a Spring AI specialist can help. The same is true when latency, security, or data access needs make a simple sample code path too fragile. In Germany, remote work is common, but on-site sessions can help when architecture and stakeholder alignment matter.
Frequently asked questions
What clients ask us most about Spring AI — answered in short.
Spring AI is used to add model-powered features to Spring applications without rebuilding the whole stack. Typical work includes chat assistants, document search, summarization, and prompt-based automation inside business systems. It fits best when a team already runs Spring Boot services and wants to extend them with generative AI.
Spring AI is usually chosen when the core application is already in the Spring ecosystem. Compared with direct API calls, it gives a cleaner structure for prompts, model clients, and retrieval flows. Compared with LangChain, it often feels more natural for Java teams that want to stay close to Spring patterns.
A strong Spring AI specialist should also know Spring Boot, REST APIs, security, testing, and data access. For production work, knowledge of embeddings, vector stores, prompt design, and observability is important. If the system uses document search, experience with retrieval-augmented generation helps a lot.
A simple proof of concept can be handled by a general Spring developer, but production work needs someone who understands model behavior and system design. Spring AI projects become harder when you need robust retrieval, secure access to company data, and predictable responses. The more critical the workflow, the more valuable a specialist becomes.
Yes, Spring AI is commonly used with OpenAI, Azure OpenAI, and other model providers. The value is that the application code can stay consistent while the provider changes underneath it. That makes it easier to compare models, switch providers, or keep a fallback path ready.
For many Spring AI tasks, remote collaboration is enough, especially for integration, prompt work, and service design. In Germany, teams often mix remote delivery with short on-site workshops when they need fast alignment on architecture or data access. The best setup depends on the sensitivity of the systems and how closely the freelancer needs to work with product and platform teams.
Look for Spring AI work that is tied to real outcomes, not just a demo. Good specialists can explain model choice, retrieval design, fallbacks, testing, and how they handle failures. Ask for examples of production code, secure data handling, and clear trade-offs rather than polished slides.
The biggest problems are weak prompts, poor document retrieval, and treating model output as if it were fully deterministic. Spring AI projects also fail when teams skip logging, ignore latency, or expose sensitive data too freely. A good specialist plans for validation, monitoring, and a fallback when the model is uncertain.
The average hourly rate of freelancers in Germany who have used Spring AI in their recent projects is 102 €, which corresponds to a daily rate of about 819 € based on an 8-hour working day.
Of the freelancers in Germany who have used Spring AI in their recent projects, 100% hold at least a Bachelor's degree and 60% hold at least a Master's degree.
On average, freelancers in Germany who have used Spring AI in their recent projects have 19 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Germany who have used Spring AI in their recent projects are German (100%), English (100%), and Bangla (13%).
The most common industries among freelancers in Germany who have used Spring AI in their recent projects are Information Technology (100%), Banking and Finance (75%), and Automotive (50%).
The most common business areas among freelancers in Germany who have used Spring AI in their recent projects are Information Technology (100%), Product Development (100%), and Operations (50%).
Main locations of FRATCH Experts, who have recently used Spring 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 Request a free demo
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Philipp Thomaschewski
FRATCH CEO
