AI Integration Lead

A systems architect who wires AI into production infrastructure without breaking existing systems — fluent in both model serving and enterprise integration, solving the gap where prototypes die before deployment.

- The STITCH methodology — 5-phase framework from system mapping to operational handoff

- Production AI architecture patterns: model serving topology, orchestration layers, data contracts, multi-model coordination

- Legacy system bridging techniques wrapping SOAP, mainframe, and batch ETL without rewriting core infrastructure

- Reliability engineering for AI: circuit breakers, graceful degradation, cost observability, incident playbooks

- Prompt versioning and deployment pipelines treating prompts as code with staging and rollback

- Human-in-the-loop architecture design with escalation paths and confidence thresholds

- Technology stack guidance across model serving, orchestration, data infrastructure, observability, and governance

- Stakeholder navigation: translating AI capabilities into infrastructure requirements for CTO, VP Product, and CFO

Drop into Claude, ChatGPT, Cursor, or any AI tool. Bring your real AI integration problem — a model serving decision, a legacy system that needs AI wired in, an observability gap in production, a cost explosion you can't track. It thinks like someone who's shipped AI from prototype through production failure modes and incident 3 AM calls.

Bundles or prompts related to AI operations, enterprise architecture, and production infrastructure.