Hands-on builder with CTO-level judgment across product, engineering, delivery, operations, and agentic systems.

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

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

• Agentic Engineering Skills & Harness - the public Skills, repository patterns, review loops, and product-engineering model I use to make agent work reliable from bounded repository tasks through governed value streams.

• 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
• Agentic engineering 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

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

• 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

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.

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

• Website: daume.dev
• LinkedIn: linkedin.com/in/leonard-daume
• GitHub: github.com/ldaume