Process Automation Experts in Berlin

In-house consulting provider and driver of transformation within the group, multi-stakeholder environment and C-level.

Realignment and stabilization of a cross-functional transformation and scaling program within the group. Sharpening the target picture, priorities, and set of measures, as well as building reliable governance, planning, and steering structures. Structuring roles, responsibilities, and strategic initiatives while including AI and IT automation ideas.

• Designed program realignment and project portfolio management

• Developed strategy model and target picture for IT projects

• Structured portfolio, roadmap, and priorities

• Established governance and regular meetings

• Worked out operating model for flagship projects

• Assessed AI and automation ideas

• Clarified roles and responsibilities

• Implemented change measures

• Developed, moderated, and evaluated workshops

• Transformed 17 initiatives into a steering model

• Increased transparency and decision-making ability

• Strengthened commitment in steering

• Sharpened the operating model structurally

• Integrated three top-5 institutes

• Involved over 80% of stakeholders

• Governance

• Portfolio steering (PPM)

• Change management

• Artificial intelligence

• Workflow automation

• AI use case assessment

• Confluence

• Jira

• Stakeholder management

Workflow-automation SaaS for operations teams (Berlin, 120 people); full-time freelance engagement reporting to the CEO: an initial 12-month interim mandate, extended twice through the AI build-out; owned product for one squad and coached the other product managers on process.

• Led generative AI (LLM) integration into the core product: from LLM-powered steps to natural-language workflow authoring and step-level automation suggestions, plus AI-managed dynamic workflows, shipped behind eval gates with human-in-the-loop fallbacks: AI-drafted workflows grew to 31% of all new workflows, and median time-to-first-workflow fell from 3 days to 4 hours.
• Packaged the AI capabilities as a usage-based add-on priced on executed automation steps, working with sales and marketing on positioning: ~€800K added ARR in the first year, and adopting accounts churned 1.8 pp less.
• Owned the roadmap end to end: replaced feature-request-driven quarterly planning with an outcome-based rolling roadmap built on quarterly bets and explicit kill criteria, presented monthly to the executive team and quarterly to the board.
• Rebuilt the product-management operating system: weekly customer-discovery cadence incl. workshop facilitation, RFC/decision-doc reviews and a single quarterly metrics narrative; coached four product managers, one promoted to senior during the engagement.
• Closed the engagement as scoped: hired and onboarded the permanent VP Product, handed over the process playbook and roadmap, and exited on schedule in June 2026.

I served as the Lead Designer for two cross-functional teams at Casavi, a software platform for property managers. Casavi is a central platform for all aspects of digital property management, which streamlines operations and business processes.

SmartTask: The team focuses on ticket management, workflow automation, AI integration, enhancing communication between property managers and their clients.

Customer app/portal: The team is dedicated to fostering community engagement among residents of managed properties. The customer portal in combination with the Casavi app solves the challenges for property and facility managers.

My role spans the full design lifecycle, covering UX/UI design, user research, usability testing, and strategic design planning, across Casavi's web platform, mobile app, and customer portals. Each team comprises around 10 members, including a project manager, designer, developers, and QA testers. I am also responsible for the accessibility assurance of the whole product.

Design unit role: In addition to my core responsibilities, I actively contribute to the development and maintenance of the company's design system and style guide ensuring consistency and scalability across all products. Mentor junior designers is my official and moral responsibility.

• Partnered with senior stakeholders across AWS EMEA to drive talent strategy, partner development, and business growth in the cloud ecosystem.
• Focus areas:
• Collaboration with Sales & Partner Management on Go-to-Market initiatives
• Advisory on long-term resource strategy for Cloud, Data, and Security Divisions
• Supporting internal innovation teams in scaling AI and automation projects
• Result: Contributed to AWS’s expansion in Central Europe by aligning business, technology, and people strategy.

Driving AI model quality at scale — evaluating prompt-response accuracy, flagging edge cases, and maintaining SLA-compliant workflows across distributed global teams.

• Analyse AI prompts and side-by-side model outputs to assess response quality, factual accuracy, relevance, consistency, and compliance with project evaluation guidelines.
• Perform fact-checking, data validation, troubleshooting, issue identification, and edge-case review to improve quality standards across AI training support workflows.
• Use Google Sheets, Google Docs, and browser-based tools to document findings, maintain evaluation logs, track issue patterns, and support workflow optimisation in a remote environment.
• Create clear written justifications, review summaries, and KPI-oriented reporting focused on accuracy, turnaround time, documentation completeness, defect identification rate, and SLA adherence.

• Independent training in travel expense accounting (Egencia, Perk, AirPlus)
• Interface management between travel expense tools
• Onboarding and offboarding of employees and vendors
• Advising teams on posting incoming and outgoing invoices
• Documentation and process improvement

• 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

• Designed and developed AI-powered document processing solutions using Python, OCR, NLP, and Large Language Models (LLMs) to automate extraction, validation, and classification of financial documents, reducing processing time by 75%.
• Built intelligent multi-stage workflow automation pipelines integrating AI services, machine learning models, and enterprise systems to streamline financial operations and improve data quality.
• Developed reusable AI-driven transformation frameworks capable of processing structured and unstructured document formats (XML, CSV, JSON, TXT, DAT) and normalizing them into unified business schemas.
• Designed and developed Python-based REST APIs and backend services supporting enterprise finance applications and high-volume data processing workloads.
• Built scalable data synchronization pipelines between Oracle CFIN and SQL databases, incorporating machine learning models for cash-flow forecasting and AP/AR anomaly detection.
• Architected and deployed Apache Airflow workflows to orchestrate AI-powered data pipelines, automating end-to-end processing from document ingestion through financial system integration.
• Led the migration of critical enterprise integrations from MuleSoft to Python-based services, improving maintainability, performance, and operational flexibility while preserving complete data integrity.
• Managed the full API lifecycle including solution design, implementation, documentation, deployment, monitoring, and production support for mission-critical financial systems.
• Collaborated directly with finance stakeholders to identify business challenges, define solution requirements, and deliver measurable operational improvements through automation and AI-driven workflows.
• Worked closely with cross-functional engineering and business teams to rapidly iterate on features, improve processes, and drive successful adoption of AI-enabled solutions.
• Provided technical leadership through architecture reviews, technology decisions, code reviews, and engineering best practices across integration and automation initiatives.
• Mentored developers, established coding standards, and contributed to improving software quality, maintainability, and delivery effectiveness across projects.
• Provided production support during critical month-end and quarter-close financial processes, performing root-cause analysis and implementing rapid fixes to ensure system reliability and data accuracy.

• Define D2C business models and go-to-market strategies for consumer businesses. Develop business cases and prioritised roadmaps that help management evaluate commercial opportunities, decide where to invest and organise implementation.
• Lead commerce replatforming from business requirements and technology selection through implementation management. Coordinate internal teams and delivery partners, structure programme dependencies and clarify responsibility for key decisions.
• Design customer experience and lifecycle marketing strategies. Translate customer needs and commercial priorities into service and conversion initiatives, platform requirements and data models, and guide technology selection and migration planning.
• Structure commerce operating models with clear responsibilities across marketing, customer experience and technology. Prepare teams to take ownership of platforms and processes, with agreed priorities and clear handovers into internal operations.
• Advise businesses on AI applications in marketing, customer service and commerce operations. Draw on experience in AI-supported automation, process design and governance, alongside contributions to local systems development.

Built and scaled a high-performance enterprise API for real-time address validation and data quality with sub-second latency and 99.9 % availability.

Designed and integrated the solution into e-commerce, checkout, and logistics processes of leading European companies. Reduced delivery errors and shipping costs through automated data correction and precise data validation.

End-to-end responsibility for product strategy, technical architecture, software development, enterprise customers, operations, and GDPR-compliant data processing. Combined AI-native engineering workflows, Python, SQL, API integration, data quality, and workflow automation.

• Led development of RunBot AI assistant autonomously using LLM-powered workflow automation (React, TypeScript, Java, MongoDB, NATS)
• Architected TMS platform providing unified transportation management and real-time logistics visibility with AI processing pipelines
• Designed event-driven microservices architecture supporting marketplace
• Drove architectural decisions and technical leadership across full-stack platform development

• Directed the contracts management function for Europe's largest renewable energy portfolio, 10 FTE
• Implemented process automations by architecting scalable, data-driven workflows leveraging AI and traditional automation

• Savills is one of the world's leading property advisors with 700+ offices worldwide. In Berlin, I built core digital solutions for the valuation business, replacing legacy Access-based workflows with an enterprise-grade web app built on Vue.js.
• Frontend Architecture & Scalability: Designed scalable Vue + TypeScript frontend architecture for internal enterprise applications, integrating a Symfony backend and interactive maps.
• Legacy Modernization: Led the modernization of legacy jQuery-based systems into maintainable Vue-driven applications, improving performance, maintainability, and efficiency across business workflows.
• PDF Report Workflow Automation: Engineered Twig-based automated PDF generation pipelines that reduced manual report preparation time by ~80%, saving approximately 12h per analyst/month.
• Design System & Frontend: Built a scalable SASS-based design system using mixins, maps, functions, and reusable UI patterns for consistency and long-term maintainability.

• Process analysis and target concept: discussions with responsible stakeholders and users, system and handover model, bottleneck analysis and acceptance criteria.
• Defined automation modules, built with AI support using Python/FastAPI, TypeScript/Next.js, SQL/PostgreSQL, Supabase, REST APIs and Webhooks; I am responsible for the specification, acceptance criteria and acceptance testing.
• Document and decision workflows from intake, research and extraction through to decision documents, CRM updates or controlled system actions.
• Quality assurance and handover with test cases, logging, exception paths, operational documentation and knowledge transfer.