AI Product & Platform Engineer · Architecture · Strategy · Governance
Native applications · AI platforms · enterprise architecture
selected work 2019 → 2026
I work across hands-on engineering and technical consulting. I design native and web products, model gateways, RAG and agent workflows, evaluation and LLMOps — and I work at the decision layer around AI strategy, architecture, governance, compliance and adoption. The goal is not a successful demo. It is a system that can be explained, operated, secured and moved into production.
Selected work across native product engineering, generative interaction systems and realtime consumer software. Each case focuses on architecture, operational constraints, implementation stack and evidence — not just the interface.
A keyboard-triggered macOS dictation pipeline with local speech recognition, optional on-device MLX processing and explicit cloud fallback. The engineering surface includes permissions, focused-app insertion, resource lifecycle and signed application updates.
An agent emits validated explainer chunks while narration and media execute asynchronously. The browser player renders the latest coherent document state before the complete artifact exists, with bounded visual recipes instead of arbitrary generated DOM.
A football companion operating under burst traffic, external API limits and rapidly changing player data. Reliability work centers on caching, background pipelines, stable identity, PWA behavior, request discipline and correctness under load.
A second portfolio layer for enterprise systems, developer tools, product experiments and applied R&D. Projects are grouped by system type and delivery evidence; forks, backups and low-signal repository history stay out.
End-to-end architecture across native/web clients, model gateways, RAG, agent workflows, identity, retrieval, data stores, deployment paths and observability. Technology choices are tied to operating constraints rather than trend adoption.
Technology assessment, build-vs-buy decisions, platform roadmaps, proof-of-value design, stakeholder workshops and translating new model capabilities into concrete engineering and product decisions.
Identity, model/provider policy, data classification, private networking, managed secrets, auditability, budgets, rate limits, human review, evaluation and deployment gates. Governance is part of the request path, not a document added after launch.
Quality gates, traceability, LLM observability, failure analysis, CI/CD, rollout planning, technical workshops and team enablement. The objective is repeatable capability, not one successful prototype.
Native and web products, backend services and shared AI infrastructure. I work across interaction design, data flow, model integration, latency, fallbacks, persistence and production failure modes.
Model gateways, provider abstraction, RAG/retrieval, agent orchestration, evaluation, telemetry, cost controls and context quality. The objective is an operable platform rather than direct SDK integrations scattered across products.
AI landscape assessment, architecture options, platform strategy, proof-of-value framing, build-vs-buy decisions, workshops and stakeholder alignment. I connect technical feasibility with product value and organizational operating models.
Identity, policy enforcement, data boundaries, security controls, auditability, evaluation, human-in-the-loop review and release gates — plus the training and documentation needed for teams to use the platform responsibly.
AI product, platform architecture or technical strategy?
Let's talk*detmers.k@gmail.com