Architect at the intersection of AI engineering, cloud platforms, and enterprise systems. I turn ambiguous goals into systems that ship — Azure-native, well-architected, and pragmatic about the boring details that decide whether something actually works in production.
LLM applications, RAG pipelines, and agent workflows. End-to-end ownership from prompt engineering and evaluation to guardrails, cost control, and production deployment on enterprise-grade infrastructure.
Azure-first solution architecture: landing zones, hub-and-spoke networking, identity, and Well-Architected designs. Cost, security, and reliability treated as first-class concerns — never afterthoughts.
Data platforms that AI workloads can actually rely on. Ingestion, transformation, governance, lineage, and serving layers tuned for analytics, BI, and machine learning.
CI/CD, IaC, and developer experience that lets teams ship fast without breaking trust. Treating MLOps, DataOps, and platform engineering as one cohesive discipline.
Translating ambiguous business goals into defensible technology roadmaps. Bridging the C-suite and the engineering team — capable of running a steering committee and a sprint review in the same day.
Designing zero-trust architectures and identity-first platforms. Threat modelling baked into design reviews; compliance treated as a feature, not a final-stage tax.
Agents should propose, not execute. A well-scoped approval gate is worth a hundred impressive demos.
Run it again. Run it a thousand times. The fingerprint of the system should not change.
GitOps isn't a slogan. Every change has an author, a diff, a reviewer, and a rollback.
Metrics, logs, and traces get designed alongside the thing — not bolted on after the first incident.
PostgreSQL, Terraform, Bicep, Helm — boring and predictable. Spend novelty budget on the composition, not the parts.