GraphQL Experts in Zurich

• 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

Reduced time-to-market by optimizing CI/CD pipelines. Employees without customer VM access can now deploy. Increased system stability and performance and reduced maintenance effort through systematic database cleanup. Restricted rights for Flyway migrations versus service users. Improved transparency and decision-making basis with enhanced Grafana dashboards for response times, optimized logging, and standardization with Loki. Reduced security risks by consistently applying security best practices such as removing the public schema in PostgreSQL, individual service user permissions, and Docker security context. Facilitated knowledge transfer and sustainable process improvements by creating process diagrams and implementing various concepts. Conducted code reviews across multiple repositories.

• Supported the team in merging two real estate platforms into a unified Vue.js-based solution
• Developed a custom SSR microservice architecture deployed on AWS Lambdas
• Designed and implemented a map drawing feature
• Took ownership of migrating the microservice from Vue 2 to Vue 3
• Built a scalable SSR microservice architecture using AWS Lambda
• Developed and implemented a critical map drawing feature for real estate visualization
• Successfully migrated the microservice from Vue 2 to Vue 3
• Gained deep knowledge of AWS infrastructure, Datadog, Sentry, and Vite
• Technologies: Vue.js, Node.js, Vite, AWS Lambda, Datadog, Sentry, JavaScript

• 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.

• 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.