Large Language Model Experts in Switzerland

• Established the Enterprise Architecture function from the ground up for Sulzer Group (~13,500 employees, CHF 3.6B revenue)
• Defined the Technical Governance framework, Target Operating Model, and IT PMO foundation now governing architecture decisions and portfolio demand across all divisions.
• Deployed SAP LeanIX as the authoritative application portfolio system of record - catalogued 500 applications group-wide (140 for Chemtech), onboarded business and IT stakeholders, and initiated integration with ServiceNow to embed architecture data into operational workflows.
• Led the divisional & group wide Application Rationalization initiative
• Decommissioned 15% of the 140 applications Chemtech portfolio, delivering CHF 627k in run-rate savings over two years and materially reducing licensing and support exposure.
• Defined and executed the divisional AI Roadmap in partnership with Sales, Legal, and R&D leadership
• Delivered 4 AI solutions into production (including Sales process harmonization and LLM-based IP conflict detection) from a portfolio of 7 prioritized initiatives.
• Built and owned the 3-year IT Strategic Roadmap for Chemtech
• Aligned cloud and technology architecture with divisional business priorities and a CHF 1.5M annual investment envelope.
• Delivered CHF 500k in cost avoidance by challenging and discontinuing funded but low-value initiatives, demonstrated governance discipline and executive influence, with zero implementation spend.

• UX research, UX concept development, and UX design for an AI dialogue system with information on events, locations, public transport timetables, and special trips. Observations, remote user tests, session recordings, analytics evaluations, RapidUserTests, prototyping, etc., using Figma/Figma Make, Claude Design, and OpenAI Codex (Design)

AI platforms

• Contributing to the training of all top 5 US large language models through multilingual ML projects, spanning RLHF, evaluations, rubrics, conversations and safety initiatives
• Working across a diverse range of training methodologies, supporting model alignment and quality improvement in multiple languages

• Led architecture and end-to-end engineering delivery for community-driven SaaS platforms serving 2M+ monthly active users.
• Architected and led the migration of a monolithic Python/FastAPI and PostgreSQL database and LangChain with codebase to an event-driven microservices architecture on AWS EKS, sustaining 10x traffic growth from ~150 RPS to 1,500+ RPS with zero re-architecture cycles.
• Defined and enforced engineering standards across services including API contracts, observability baselines, and deployment topology, reducing production incidents by 55% and MTTR from 2 hours to under 25 minutes within 9 months.
• Redesigned the caching and query layer using multi-tier Redis caching and database indexing/partitioning, cutting p95 API latency from 850ms to 180ms (78% reduction) and database CPU load by 45%.
• Built CI/CD platform on GitHub Actions, Terraform, and Kubernetes (EKS) with blue-green and canary rollouts, increasing deployment frequency from ~2/month to 8-12/day and reducing lead time from 10 days to under 6 hours.
• Implemented contract testing, automated load testing, and observability SLOs using Prometheus, Grafana, and OpenTelemetry, raising platform availability from 99.5% to 99.95% (10x reduction in error budget burn).
• Led and grew a cross-functional team of 8 engineers across backend, frontend, and DevOps, scaling headcount from 4 to 8 with 85% retention; owned hiring, onboarding, performance reviews, and career development.
• Partnered with Product, Design, and Client Success leadership as primary technical decision-maker; translated business goals into technical roadmaps and drove build-vs-buy decisions on authentication, search, and AI tooling.
• Introduced AI-assisted development workflows including automated code review and a RAG-based internal knowledge assistant using Graph (GraphRAG, Neo4J), increasing sprint throughput by 30% across two quarters.
• Owned incident command and production support rotation; established runbooks, postmortem culture, and on-call SLOs, reducing weekend paging incidents by 70%.
• Developed and optimized Algorithms using python libraries like Numpy and Pandas.

Responsible for designing and delivering a company-wide, comprehensive Infrastructure-as-Code (IaC) transformation in a regulated banking environment.

• Conducted an assessment of the current infrastructure, processes, tools, teams, ways of working and skill profiles as a basis for the target architecture and a PDO (Product-Driven Organization)

• Defined a tailored, audit-ready Everything-as-Code infrastructure (including governance, security and compliance)

• Built a comprehensive IaC operating model, integrating and transforming internal teams

• Designed standardised, reusable Terraform modules for infrastructure and platform services

• Integrated secrets management, PKI and Encryption-as-a-Service to secure critical VMware and Kubernetes platform components and stored data

• Implemented the platform in line with regulatory requirements (DORA, BaFin, ACPR, CRA), with a focus on auditability and traceability

The initiative provides the foundation for standardised, automated and audit-ready infrastructure, reduces manual intervention and enables scalable, traceable delivery of IT resources in a banking environment.

• Intelligent Document Processing Engine with OCR & Computer Vision: Architected the end-to-end AI pipeline at the core of the SQPS GenAI document-processing product, which turns scanned paper and PDF batches into organised, searchable documents, combining image pre-processing, OCR and layout analysis for mixed-quality scans. Technologies: Python, OpenCV, Tesseract / PaddleOCR, PyTorch, FastAPI
• Automatic Document Splitting & Classification: Developed models that detect where one document ends and the next begins inside a single multi-document scan, and classify each one by type (invoices, contracts, letters, reports) to enable fully automated filing. Technologies: Python, PyTorch, scikit-learn, Hugging Face Transformers
• Information Extraction & Intelligent File Naming with LLMs: Implemented an LLM-based GenAI extraction system that reads dates, names and reference numbers from unstructured documents and uses them to generate consistent, rule-based file names, with configurable naming conventions per organisation. Technologies: Python, LangChain, OpenAI, SentenceTransformers, SQL

• Product strategy, architecture, and technical co-direction
• AI workshops, client onboarding and go-to-market activities
• Design and implementation of AI architectures (LLMs, RAG, MCP, agents, voice, automation)
• Hands-on development of prototypes and production-ready AI integrations
• Consulting clients on AI adoption, workflows, and integration into existing infrastructure
• Collaboration with co-founders on strategic direction
• Collaboration in sales and customer acquisition

Tools and Technologies: OpenAI, Anthropic, Azure, Vercel AI SDK, RAG (Retrieval-Augmented Generation), MCP (Model Context Protocol), FastAgent, VAPI, n8n, make.com, LibreChat, PostgreSQL, OpenAPI, Next.js, Vercel, Docker

• Built and explored applied GenAI, RAG, GraphRAG, local LLM, agentic AI and document-intelligence prototypes for structured analysis, evidence extraction, semantic search, technical reasoning and decision-useful reporting
• Developed private local-LLM workflows and AI system patterns focused on privacy, reproducibility, reviewability, low-cost inference and practical user control
• Built reproducible Python/R workflows for data analysis, automation, API-driven tooling, validation logic, technical documentation and AI-assisted software development
• Designed workflows around explicit assumptions, traceable inputs, reviewable outputs and failure-mode awareness rather than black-box “looks good” demonstrations
• Supported RAHN AG in a chemical/regulatory environment with data extraction and processing around WERCS, a regulatory application for chemical product and compliance data
• Explored complex application/database schemas and wrote nested SQL queries to extract information for mixture calculations, component relationships, regulatory rules and reporting logic
• Continued hands-on development in Git/GitHub/GitLab/Bitbucket, Docker/Linux deployment patterns, REST/API workflows, error handling, technical writing and fast AI-assisted prototyping
• Built technical writing and documentation workflows that turn complex systems into clear runbooks, checklists, decision notes and user-facing explanations

• Architected and implemented a Data Value Framework as a decision-making basis for strategic prioritization and determining data-driven value contributions along the corporate value chain, connecting data value with business objectives.
• Designed standardized use cases to assess the maturity levels for quantifying business value and data value.
• Integrated the framework within SAFe structures in Lean Portfolio Management and ARTs to guide strategic investment decisions.
• Developed metrics and evaluation procedures for prioritizing business areas based on quantitative data.
• Redesigned data governance, including the introduction of the data owner role with clearly defined responsibilities and differentiation from the data steward role.
• Derived MVP priorities using gap analyses across key areas and value stream analysis.

• Business analysis, architecture, and implementation of a telemedicine platform for pet owners.
• Leading the development team as Product Owner and Tech Lead.
• Introducing agile processes, setting up development guidelines and system documentation.
• Planning and implementing features like video calls, live chat, and marketing automation.
• Implementing AI-based features such as automated tagging of information (missing pet reports, marketplace entries, etc.), preparation of social media content, and processing of conversation transcripts.
• Skills: Angular, NGXS, Tailwind, Java, Spring Boot, Kubernetes, Docker, CI/CD, MySQL, LLMs, RAG, MCP, Redis, OpenSearch.
• Industry: Veterinary medicine.

• Implementations of a code refactoring framework able to refactor thousands of repositories leveraging Generative AI
• Use Python (Django, Flask, FastAPI), Java and Typescript in Azure Cloud (Data Lake, VMs, AI) and GitLab infrastructure
• Mentoring and pair programming
• Obtained Azure AI-900 and AI-102 certifications

• Designed large-scale systems and APIs serving thousands of concurrent users.
• Refactored a 650k-LOC monolith and led full AWS migration for stable performance.
• Introduced SLO-based observability, improving reliability and recovery flow.
• Optimised cloud and databases, achieving significant cost and latency reduction.
• Delivered LLM, RAG, and document-automation pipelines adopted in production.

• Co-teach in CAS AI Management and CAS AI Innovation programs for future AI managers
• Cover topics including data platforms, AI architecture, technology adoption foundations, and factors influencing enterprise AI initiative success

• Deployed and optimized the inference stack on a multi-node DGX B200 cluster across vLLM and SGLang (serving, throughput and latency tuning).
• Built, with a small team, an internal Python library for LM pretraining covering the full training loop: distributed training with PyTorch FSDP, data pipelines, checkpointing, config and hyperparameter management, and experiment tracking.
• Built and evaluated agent scaffolds on interactive game benchmarks similar to ARC-AGI-3, with metrics for how models plan, explore and adapt across multi-step episodes; classified model errors and fed the findings back into scaffold and evaluation design.
• Researched looped transformer architectures.