FastAPI Experts in Germany

• Claude Code subagents, MCP, Pydantic V2, pytest, bandit

• Designed and shipped 3 specialized agents that hand work down a line: a research agent writes a cited implementation spec, a coding agent builds the modular code and its tests, a review agent ranks findings by severity and applies the fixes. Each handoff is a structured document, so no stage depends on another agent's context window.

• Connected the research agent to an academic-research MCP server (Semantic Scholar, ArXiv, Hugging Face Hub, citation snowballing) so every reference traces to a tool result rather than the model. Gated commits behind ruff, mypy, pytest and bandit, required human sign-off before installs and commits, and persisted session state on disk so long runs survive a context reset.

Project: Defect Generation on Test-Bench Images of Metal Surfaces Environment: Automated Visual Inspection (AVI), Metallurgy & Manufacturing

• Objective & Implementation: Designed, architected, and trained Generative Adversarial Networks (Pix2PixHD / SPADE) for image-to-image transformation. Targeted generation of synthetic material defects (e.g., cracks, inclusions, scale) on rough metal surfaces under real test-bench lighting conditions for privacy-compliant and efficient dataset expansion (data augmentation).
• Technical Design: Implemented robust Generative AI and computer vision pipelines in Python and PyTorch. Used semantic segmentation approaches for mask-controlled defect synthesis and subsequent evaluation with EfficientDet object detection models.
• Business Impact: Massive dataset upscaling (10x) without time-consuming and costly physical test-bench runs, while significantly improving the detection performance of automated inspection systems.

Technologies & Skills Used: Python | PyTorch | SPADE | Pix2PixHD | EfficientDet | Machine Learning | Semantic Segmentation | Computer Vision

Maturing their API practices on both a business and technology level. My role covers strategy, architecture, developer advocacy as well as hands-on software engineering, enabling both technical teams and business leadership to adopt and act on API-centric principles effectively. Coincidentally, we also establish GitOps, DX and platform best practices with this project.

Highlights:

• Aligning executives with the initiative by clarifying strategy, replacing misconceptions and myths with facts, clarifying the value of existing assets and enabling informed decision-making
• Formulating a way forward for API Lifecycle Management at OTTO
• Driving platform progress and fostering developer engagement by hands-on engineering work towards strategic goals

API Lifecycle Management, Team Topologies, Organizational Evolution, Regulatory, Platform Advocate, Developer Platform, Communities of Practice, Terraform, Kotlin, Kafka, Kong, WSO2, Apigee, Gravitee, Backstage, AsyncAPI, OpenAPI, API Design, AWS, React, Node.js, TypeScript, Redocly, reactive programming, CDC, Golang, Gin, GitOps, DX (developer experience), stakeholder management, roadmaps, workshops, discovery.

Remote freelance role focused on Agentic AI strategy, secure application patterns, and reusable agentic workflows for a government agency.

• Development and implementation of an open source Agentic AI strategy for a government agency, with a focus on GDPR, security, and self hosted solutions
• Development of reusable agentic workflows and mini applications that enable non technical employees to solve business problems independently
• Implementation of internal business applications with Single Sign On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
• Implementation of nine mini applications with Single Sign On (SSO) and Azure PostgreSQL integration on Hetzner Linux servers
• Techstack: Python, Nextjs, Typescript, Streamlit, Anthropic SDK (Claude), Azure, Linux Ubuntu, PostgreSQL, MS SQL, Angular, Authentik

• Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
• Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
• Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.

• This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.

Used: Python, RAG, LangGraph, LangChain, deepeval, MCP

• Building a data integration platform for high, medium, and low voltage assets for contextual analysis of time series with master data from the SCADA control system (IEC 60870 104), INIS, and SAP.
• Responsibility for the architecture and implementation of the solution, as well as sparring partner for the Product Owner.
• Use of Kotlin, Spring Boot, Maven, TimescaleDB, PostgreSQL, liquibase, Elements IoT, Docker, Kubernetes, Grafana, Python, jupyter, and various API gateways.

• Designed and developed a multi-tenant SaaS platform enabling organizations to build their own knowledge bases and chat with brand-customized AI assistants (white-label approach with dynamic branding per organization).
• Implemented a scalable RAG architecture with a GPT-4o tool-use loop, hybrid semantic search, and strict tenant isolation at database and search index level.
• Built persistent, project-like chat sessions including a streaming API (SSE), multilingual support, and speech input/output (STT/TTS).
• Delivered the cloud infrastructure as Infrastructure-as-Code, fully automated per-customer CI/CD pipelines, and an onboarding process for new tenants.

Technologies used: Python, FastAPI, Pydantic (v2 noted), Next.js, React, TypeScript, Tailwind CSS, OpenAI / LLMs (GPT-4o), Azure AI Search, Cosmos DB, Azure Blob Storage, Azure Cognitive Services Speech, Azure App Service, Azure Container Registry, Retrieval-Augmented Generation (RAG), Server-Sent Events (SSE), Docker, Terraform, GitHub Actions, REST, OpenID Connect (OIDC), Multi-Tenancy

Scope: Embedded as FP&A Data & AI Architect within the Finance function of a major European refrigeration manufacturer, leading the transformation of manual, fragmented financial reporting into an automated, governance-driven intelligence platform. Driving the shift from Excel-based controlling to structured data architecture, Power BI analytics, and AI-assisted financial operations.

Financial Data Integrity & ERP Governance

• Initiated and led GL vs. subledger reconciliation investigations, identifying and resolving structural mismatches between General Ledger and subledger data that had gone undetected prior to engagement
• Conducted asset analysis to identify items missing from General Ledger postings, surfacing gaps in fixed asset tracking and period-end completeness
• Validated SAP reports, establishing baseline data quality standards for Finance team consumption
• Established systematic SAP data validation framework ensuring ongoing integrity between ERP postings and downstream reporting outputs

Finance Reporting Transformation

• Designed and implemented a structured Transformation Project approach for converting manual Finance reports into fully automated processes
• Created and owns the Data Reporting Audit Log; a centralized tracking system capturing report owners, stakeholders, data sources, manual effort estimates, and automation opportunity scores across the Finance function
• Mapped the full reporting landscape identifying quick-win automation targets and strategic Power BI migration candidates
• Actively reducing manual Excel and PowerPoint dependency across FP&A workflows; replacing point-in-time snapshots with live, governed data models

Power BI & Analytics Enablement

• Introduced and presented Power BI as the strategic reporting platform to Finance leadership, building internal buy-in for the BI transformation roadmap
• Designed initial Power BI architecture aligned with SAP, Salesforce and Oracle data structures and FP&A reporting requirements
• Established report ownership, governance documentation, and data lineage standards enabling sustainable self-service analytics across the Finance team

Transformation Infrastructure & Collaboration

• Configured and deployed Jira as the transformation project management hub, establishing structured sprint workflows, backlog management, and progress visibility for Finance IT initiatives
• Proposed and initiated a dedicated FP&A Communication & Transformation Hub, a structured cross-functional forum aligning Finance, IT, and business stakeholders around the reporting transformation roadmap
• Positioned the Finance function as an active driver of data governance and digital transformation within the broader organization

Outcomes

• GL/subledger reconciliation gaps identified and investigation framework established within first two weeks of engagement
• Data Reporting Audit Log deployed; first structured inventory of Finance reporting landscape in company history
• Power BI transformation roadmap presented and approved by Finance leadership
• Jira-based project governance live; Finance transformation now tracked with full sprint visibility

Technologies: SAP FI/CO · Power BI · DAX · SQL · Excel (advanced) · Power Query (M) · Power Automate · VBA · Jira · Microsoft 365 · SharePoint · Salesforce (Sales Data) · Oracle HCM · Python

Temporary employment

• System black-box integration tests (BBIT, IVVQ): Execution of regression, release, acceptance, and compliance tests for safety-critical brake control units in the rail industry
• Software test application & integration: Runtime configuration of software components and libraries, validation of interfaces, configuration dependencies, and component interactions
• Test automation (FEAT framework): Co-development and further development of an automated test framework for test execution, reporting, and result analysis
• Functional safety (SiL4, FuSi): Ensuring compliance with safety requirements, traceability and coverage, as well as standards compliance according to EN50126/28/29
• Test automation for communication components: Configuration and validation of fieldbus (CAN) and Ethernet-based TCMS data communication interfaces (TRDP and CIP)
• Requirements analysis & shift-left (PTC Windchill ALM): Analysis of software and system artifacts to identify gaps, ambiguities, and redundancies early in the SDLC
• Test design & test case development: Derivation of test conditions, coverage strategies, and implementation of data-driven test cases (DDT), including reusable test data fixtures
• CI/CD & automation (Python, PowerShell, Jenkins, SVN): Automation of build, test, and HIL deployment processes as well as integration into CI/CD pipelines
• Test data & configuration management (XML): Maintenance and adaptation of XML test vectors and system configurations with automated integration into test environments
• Non-functional testing: Execution of performance and load tests to assess stability and system behavior
• Agile development & defect management (JIRA, Confluence): Participation in Scrum teams, test coordination, review of test artifacts, as well as defect tracking and root-cause analysis
• Error analysis & debugging (CANoe, CANalyzer): Analysis of errors and message flows across multiple system layers (application to bus)
• Model-based analysis (UML, Enterprise Architect): Specification of SUT/SOW and support for systematic test control
• Process & test documentation: Creation of integration and test documentation according to internal quality and certification requirements

• Created complex backend systems (Fastapi Python, GCP cloud functions, APIs, integration tests) using Typescript.
• Worked with firebase and firestore databases, implementing transactional operations, scheduling jobs, and migrations.
• Implemented GCP dashboards for thorough monitoring and custom alerts in case of anomaly traffic.
• Implemented Sentry for better debugging, error tracking and overall monitoring of the Next.js frontend.
• Implemented story tests for UI related testing.
• Implemented Typesense in Python Fastapi backend, for improved text based searching along with typo handlings.

Task:

• Design and build a proof of concept (PoC) for a future-proof virtualization platform, taking secure system architectures into account
• Assess the current state of existing infrastructures and develop selection and evaluation criteria for the right OS virtualization platform
• Carry out the requirements analysis and then create and prioritize tickets in the ticket system
• Complete and continuously update a tool evaluation matrix based on PoC results
• Support team knowledge building through clear documentation of the approach and results in Confluence
• Enterprise analysis of existing hardware (creating different BoMs)

Technologies: Vmware, Vmware Aria Operations, Osism, Canonical OpenStack, FishOs, Linux, Terraform, Ansible, Confluence, Alma

Built Python-based AI microservices and integrations for an AEC/VR Unity-based SaaS app, focusing on LLM/VLM capabilities, retrieval-backed systems, RESTful APIs, containerized deployment, and an automation microservice for the CAD-to-Unity pipeline.

• Developed a custom Hybrid A* based algorithm in C# to simulate hospital scenarios and detect early-stage design conflicts from collision/spatial data and generate structured reports.
• Solved and automated the time-consuming problem of converting CAD files to usable Unity environments with a custom-engineered and real-time pipeline using a ZeroMQ-based communication layer to distribute workloads across multiple processes and achieve real-time performance.
• Built a Dockerized FastAPI pipeline for CAD-to-Unity automation, combining vision-based object matching, image embeddings, and precomputed metadata to automatically map CAD objects to Unity behavior scripts, assign properties, and reduce repeated AI inference calls.
• Created documentation and examples to help technical users understand, configure, and extend the AI automation pipeline.

Conversational AI assistant for cloud infrastructure and security queries

• Designed FastAPI backend with multi-turn conversation handler, token budgeting, and context window management.
• Integrated Amazon Bedrock (Claude 3 Sonnet/Haiku); built RAG pipeline with MongoDB chat history and semantic search.
• Implemented Factory Pattern for modular LLM provider switching; reduced model onboarding effort by 60%.
• Reduced LLM inference cost by 35% through model tiering (Haiku vs Sonnet) and prompt/entity consolidation.

Tech: Python, FastAPI, Amazon Bedrock, MongoDB, Streamlit, Pydantic.