Leonard "Lenny" Daume

AI-native builder with CTO-level impact.

I build product engineering systems: architecture, backend and data services, delivery flow, platform operations, and AI-native work with clear ownership.

Project context, CV material, and contact details are on daume.dev.

What I work on

Product and engineering systems that connect strategy, architecture, delivery, quality, and operations
Web and product surfaces where UX, domain logic, APIs, and runtime behavior have to fit together
Backend and data services: APIs, workers, queues, geodata, search, reporting, and persistence
AI-native delivery with clear context boundaries, quality checks, traceable decisions, and responsible ownership
Delivery systems: monorepos, CI/CD, release paths, observability, documentation, and operational feedback
Engineering ownership models where teams can decide close to the work without losing responsibility for impact, security, privacy, or production behavior
0-to-1 platforms, rebuilds, scale-up contexts, and technical direction that stays close to implementation

Current technical focus

I currently work hands-on as CTO on a product system where product logic, data workflows, backend services, UI work, and operations sit close together.

The current system work includes:

product and UI work in a pnpm/Turborepo setup with web app, services, workers, and operating tools
backend and worker flows with Bun, BullMQ, Redis, typed APIs, and explicit job-completion signals
geo, routing, and census services with Kotlin, Spring Boot, PostGIS, OpenAPI, FastAPI, Valhalla, OSM, and external POI imports
product research with PCA, k-means, benchmarking, residualization, feature ablation, and evidence gates
reporting, export, demo, analytics, and internal planning paths that connect product learning with delivery
platform operations across deployment, auth, backups, observability, incident visibility, and recovery paths

Selected proof signals

Remotely scaled delivery across 15 teams and 100+ developers, using flow signals, golden paths, cloud-native delivery, and stronger operational ownership
Built an AI-supported freelancer-project matching platform from 0 to 1 with product, technology, and team setup in one responsibility
Built a health-study SaaS from product model through backend, frontend, CI/CD, team setup, and operations
Built SERP parser and crawler systems in a SaaS SEO analytics context, including parser work for 450M+ keywords worldwide
Led backend, delivery, and architecture work for a big-data SEO platform with Java/Scala, microservices, multi-database architecture, AWS/Docker platform work, and team leadership
Built enterprise Java systems, SMS infrastructure, portal work, and data-center operations experience into a practical "you build it, you help run it" engineering stance

Engineering stance

Software development is not plan execution. It is product and system evolution under uncertainty.

Good engineering connects:

product direction
engineering judgment
delivery flow
operational feedback
security and privacy by design
readable systems
built-in quality
team ownership

Ownership does not mean heroics. It means making responsible decisions because the system gives people enough context, clear boundaries, and support when risk is shared.

I use AI in engineering as practical support for research, delivery, documentation, and review. Ownership for the result stays with the humans doing the work.

Technical range

These are recurring parts of the work:

Languages: TypeScript, JavaScript, Java, Kotlin, Python, Scala
Web and product: Astro, React, Vue, Svelte, Next.js, PWA patterns, REST/OpenAPI
Backend and data: Spring Boot, Play Framework, FastAPI, Bun, BullMQ, PostgreSQL, PostGIS, MySQL, ArangoDB, MongoDB, Redis
Delivery and operations: CI/CD, Jenkins, GitHub Actions, Docker, Kubernetes, deployment automation, observability, alerting, backups
AI and data work: workflow automation, research tooling, PCA, k-means, NLP, feature ablation, evidence gates