Prompt Engineering Experts in Zurich

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.

• 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

• Build prototypes, expand AI skills, and practical application in a startup context.
• Transition phase: further training in prompt engineering, APIs, AI, working on independent prototype projects, and collaborating at a friend's startup.
• Technologies: LangChain, REST, Python, Java, JS, TS, Node.

• 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.