Role-Based Access Control Experts in Berlin

• Built a customized MediaWiki instance, including installation, MySQL database, SSL, and automatic backups
• Set up user roles (Admin, Mod, Verified, User) and a permissions system
• FlaggedRevisions for editorial review workflows · Commenting and rating extensions

• Opt-in RAG extension over tenant-owned data sources (SharePoint, Confluence) on existing multi-tenant enterprise AI platform
• Architecture: ADRs, solution evaluations, permission strategy, cost modeling
• End-to-end SharePoint and Confluence connectors: OAuth consent flows, token refresh, metadata sync, search integration
• Permission resolution: ACL indexing at ingestion and query-time verification (document-level security)
• Elasticsearch hybrid search (semantic + keyword) with RRF scoring
• RAG chat with context injection and source citations
• Stack: Elastic Cloud, Elasticsearch, Docker, Northflank, Next.js, React, TypeScript, Prisma, Microsoft Graph API, Atlassian REST API, OAuth 2.0

• ▶ Built real-time voice assistant (STT → LLM → TTS pipeline) benchmarking and evaluating multiple STT providers including faster-whisper and Azure Speech. achieved sub-3s latency, Groq API (Llama 3) with multi-turn memory - directly handling edge cases in dictation, names and passcode recognition.

• Azure Landing Zone architecture: Design and implement a hub-spoke, multi-region-capable Azure landing zone that provides the secure, scalable and highly available foundation for autonomous driving and parking data/ML platforms.
• Compliance through Azure Policy: Enforce security and compliance standards across Azure subscriptions via Azure Policy (guardrails, deny/audit/deploy-if-not-exists), consistently achieving top-tier infrastructure security scores.
• Infrastructure-as-Code: Automate provisioning and configuration of Azure resources with Terraform and Bicep, with changes delivered through reviewed CI/CD pipelines.
• Azure Databricks and Unity Catalog: Extensive experience operating Azure Databricks with Unity Catalog for centralized data governance, access control, lineage and auditing across workspaces.
• Monitoring and observability: Built and run infrastructure monitoring and logging with Azure Monitor, Prometheus, Grafana and Loki, covering platform health, alerting, troubleshooting and capacity trends.
• Platform at scale: Architected and operate cloud data/ML platforms supporting multi-PB vehicle data ingestion and simulation; orchestrate Argo Workflow-based recompute and autolabeling services scaling to 100k+ concurrent pods on AKS.
• Reproducible ML: Established reproducible training environments with dataset versioning and provenance tracking, enabling auditing and experiment reliability.
• Operational excellence: Automated GitHub organization management and CI/CD pipelines, improving developer productivity and deployment reliability; automated recurring operational processes.
• FinOps: Developed transparent resource utilization and cost-tracking for multi-project Azure environments.
• Leadership: Technical lead for infrastructure topics, collaborating with development, DevOps and security teams; technical interviewer and recruitment panel member for scaling the engineering team.

• Led the end-to-end delivery of enterprise full-stack web applications by driving requirement analysis, solution design, frontend and backend development, database design, API integration, code reviews, team coordination, Agile execution, CI/CD deployments, production support, performance optimization, security implementation, and stakeholder collaboration to deliver scalable, high-quality software solutions.

• Founded an AI-powered scheduling and business-management SaaS for SMBs, owning technology strategy, architecture, product development, UX, billing and go-to-market execution.
• Architected and shipped a multi-tenant platform with REST APIs, RBAC, CRM, billing and notifications, powering the manager dashboard, admin console, booking experience and iOS/Android applications.
• Led and mentored 7 software engineers, 1 DevOps engineer, 1 QA engineer and 1 UX/UI designer, while remaining hands-on across backend, frontend and product delivery.
• Built AI voice and chat agents using Python/FastAPI, OpenAI and Anthropic APIs, RAG, pgvector and tool calling; integrated Twilio, Google Calendar/Meet, Stripe and Firebase.
• Owned production infrastructure and automated delivery across separate environments using Docker, Nginx, GitHub Actions and Grafana; represented the company at accelerators and international startup events.

Selected stack: Python, FastAPI, Node.js, Vue 3, React/Next.js, React Native, PostgreSQL, Redis, Docker

Company in the industrial sector

Projects completed during retraining

• Rebuilt the company website from WordPress using a modern stack with Astro and TypeScript, deployed on Vercel (live at sybarium.vercel.app); replaced the legacy CMS with a faster, maintainable system.
• Reduced page load time by ~80% (from 5 s to under 1 s) through optimization, caching, and asset optimization.
• Developed n8n automation workflows that integrate several REST APIs and internal systems, saving more than 3 hours of manual work per week.

• Codebase Evaluation & Problem Design: Designed and stress-tested complex software engineering problems against large open-source Python codebases (including pandas), requiring deep context acquisition and architectural understanding to produce well-scoped, realistic problem statements aligned to strict correctness guidelines.
• Agent Failure Analysis: Assessed LLM coding agent solutions for correctness and completeness, identifying meaningful failures across edge case handling, dtype behaviour, and multi-column NaN propagation logic; documented findings with precision for downstream evaluation use.
• Programmatic Test Suite Development: Authored comprehensive pytest suites to programmatically verify agent-generated solutions against defined requirements, with deliberate coverage of boundary conditions and failure modes not caught by naive implementations.
• Containerised Environment Engineering: Built and debugged Docker environments for reproducible agent execution, including git-based repository provisioning, dependency pinning with npm ci, and multi-stage Dockerfile authoring across Linux-based containers.

• Insurance service provider*

Challenge: Business-critical documents were processed through external OCR providers, with ongoing costs, dependency, and data privacy risks for sensitive insurance data.

Implementation:

• Architecture and production implementation of an on-premise OCR solution with full data ownership
• Methods for recognizing document structures as the basis for automated further processing
• ML-, NLP-, and LLM/VLM-based information extraction, especially from invoices and quotations

Success: Replaced external providers: full data ownership, GDPR-compliant processing, and 75% lower recurring OCR costs per year

Used technologies: Python, Docker, Microservices, FastAPI, PyTorch, Torchvision, MongoDB, MySQL

• 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

• Too Good To Throw (AI powered social impact webapp focused on reducing food waste in Nigeria):

• Integrated Paystack Split Payments to automatically route payments between the platform and partner vendors.

• Configured automated subaccount creation workflows so new businesses get a settlement account instantly.

• Setup a scalable cloud backend using Supabase.

• Implemented role-based access control (RBAC) for Users, Partners, and Admin.

• FaithFlow (AI powered webapp supporting Christian teens on their spiritual journey):

• Designed and implemented an AI-driven scripture search engine that interprets natural language questions and maps them to relevant Bible texts, commentary, devotionals, and cross-references.

• Designed a spiritual growth dashboard enabling users to track reading progress, prayer streaks, and devotional completion milestones.

• Developed and implemented organization-wide discovery framework based on Ulwick’s Outcome-Driven Innovation; enabled 7 Product Owners to systematically identify and quantify unrealized value through shared outcome language and opportunity scoring methodology
• Transformed Product Owner role from backlog clerks to strategic experimenters; established dedicated time budget for autonomous hypothesis testing and discovery activities
• Rebuilt customer journey maps to start at actual user need (tool selection phase) instead of platform entry point; eliminated manual data aggregation work previously done by project teams
• Implemented OKR framework across 4 product teams; defined quarterly objectives with measurable key results (e.g., 40% reduction in manual integration effort, self-service adoption increase)
• Unified 3 separate platform roadmaps through cross-team dependency mapping and shared service agreements
• Supported enterprise sales cycle with ROI modeling and technical due diligence for automotive and defense customers

• Establishing Identity & Directory Management as a new (split-off) team & product within the IAM cluster.
• Leading the “Identity & Directory Management” product (8 people) as Product Owner.
• Concept for ‘Digital Identities’, i.e. IDs with n users/accounts and strategy for a modernized product offering.
• Upgrading APIs, migrating to a containerized infrastructure, rolling out international markets & standardized solutions across the REWE enterprise group.
• Tech: OpenText™ (NetIQ) eDirectory & Identity Manager, LDAP, SAP HR/HCM, Docker/Kubernetes/Podman, REST APIs, Microsoft Active Directory & Entra ID, postgresDB, Keycloak, Ansible, Cyberark (PAM), Apache Kafka, Jira, Confluence, Miro.

Java 21 · Spring Boot 3 · Keycloak · PostgreSQL · Docker · Nginx · GitHub Actions · DigitalOcean · React 18 · TypeScript · Plasmo

• Architected and shipped a production-ready Job Application Tracker end-to-end: REST API with 5-stage workflow, pagination, sorting, and dynamic filtering — full ownership from design to live cloud deployment on DigitalOcean.
• Implemented production-grade identity management: OAuth 2.0 / OpenID Connect / JWT / RBAC via Keycloak, applying Hexagonal Architecture and DDD principles.
• Built automated CI/CD pipeline (GitHub Actions); containerised with Docker; Nginx reverse proxy with path-based routing and SSL termination.
• Developed a Chrome Extension (Plasmo framework, Manifest V3) that auto-fills job applications directly from LinkedIn into the tracker — demonstrates full product thinking across backend API and browser client.