AI Governance··3 min read
The AI SDLC: How Companies Should Build, Test, Govern, and Operate AI Systems
A practical draft outline for a future article. It is intentionally marked as draft content.
Swiss and European AI adoption
Helping companies adopt AI safely, effectively, and with strong data and engineering foundations.
Enterprise architect and data/AI executive with 25+ years of experience designing, governing, and delivering platform and application architectures in highly regulated environments including banking, insurance, and life sciences. Trusted advisor to CIO/CDO, risk, audit, and security stakeholders on target-state architecture, technology standards, transformation roadmaps, regulatory evidence, and safe AI adoption.
Expertise pillars
Enterprise data and AI architecture
25+ years designing and governing platform, application, data, and AI architectures in regulated environments.
Hybrid cloud and platform foundations
On-prem and Azure architecture patterns, identity, security, platform services, Databricks/Delta Lake, IaC, CI/CD, and observability.
Architecture governance
Principles, standards, reference architectures, ARB/Tech Council cadence, ADRs, exception handling, and evidence packs.
Regulatory evidence and lineage
BCBS 239 alignment, audit remediation, lineage, controls as evidence, and model risk / AI assurance discussions.
Engineering leadership at scale
Built and led a 60+ FTE Data Engineering & ML practice with 11 direct reports and CHF 20M+ portfolio responsibility.
Application landscape management
Capability mapping, dependency mapping, lifecycle state, technology health, technical debt scoring, and transition roadmaps.
What I Help With
AI Readiness Assessment
Unclear readiness, scattered pilots, weak data foundations, and uncertain risk ownership.
Corporate AI Governance Review
Unclear policies, approval paths, model risk ownership, and audit evidence.
AI SDLC and Architecture Review
Fragile prompts, weak evaluation, missing monitoring, and poor release discipline.
Data Engineering Foundation Review
Unreliable pipelines, unclear ownership, missing quality checks, and weak lineage.
Engineering Management Advisory
Delivery friction, unclear priorities, overloaded leads, and weak operating rhythms.
Swiss Data/AI Staffing Advisory
Mis-scoped roles, weak interview loops, and unclear talent trade-offs.
Solopreneur AI Workflow Design
Tool overload, brittle workflows, and unclear measurement of AI value.
Featured Essays
Practical writing for leaders and builders.
Topic Map
The site is organized around answerable business and engineering questions.
AI Governance
AI governance helps companies use AI safely by defining ownership, controls, metrics, and review paths.
AI Architecture
AI architecture connects business goals with data, model, governance, and platform decisions.
AI SDLC
An AI SDLC makes AI delivery testable, governable, observable, and maintainable.
Data Engineering
Strong data engineering is the foundation for credible AI, analytics, and operational decision-making.
Data Lineage
Data lineage creates traceability from source systems to reports, models, decisions, and AI outputs.
Engineering Management
Engineering management turns technical ambition into repeatable delivery and healthier teams.
Swiss Data Job Market
Swiss data staffing works better when companies define roles, evaluation criteria, and team context clearly.
Human + AI Collaboration
Human-AI collaboration is about workflow design, not just tool access.
Truth in a Post-AI World
Post-AI trust depends on provenance, verification, and transparent decision records.
Stay in touch
Newsletter and contact channel.
Longer essays and future paid content will move through Substack. This site keeps the canonical expertise map, consulting pages, and meeting request flow.