Own the end-to-end architecture of the AI-assisted migration platform — agent/skill/harness design, AI-in-the-loop bounding of LLM output, and Azure cloud architecture (App Services, Functions, Key Vault, Azure AD). Drive AI/ML strategy: build-vs-buy and model selection calls, fine-tuning vs. prompting and cost/latency trade-offs, and definition of fidelity scoring and gap-analysis evaluation metrics. Lead delivery and stakeholder management — scope feasibility studies vs. production builds, define data requirements with customer IT teams, and triage client edge-case escalations into a prioritised backlog. Lead and mentor a 3–8 person AI/automation engineering team across classical and prompt-engineering disciplines, while owning migration risk, rollback strategy, and security/credential review. Develop AI/LLM components — prompt engineering with schema-constrained output, LLM API integration (Anthropic/OpenAI/Azure OpenAI), and RAG or classification pipelines that map legacy actions to modern equivalents. Integrate the solution with the surrounding ecosystem via REST APIs, webhooks, Azure CLI/Azure AD authentication flows, and MCP server endpoints. Own quality through automated workflow validation tests and eval harnesses that score model output fidelity, and document unsupported or manually reviewed conversion cases. Build and maintain the parsing/conversion engine (Node.js/TypeScript or Python) that reads XML/JSON-based Nintex workflow definitions and generates equivalent Power Automate flow logic.   AI Developing Skill: Core Programming & Parsing Node.js / TypeScript or Python (framework matching the Agent's stack) XML/JSON parsing experience (Nintex NWF is XML-based) Experience building parsers, ASTs, or rule engines Regex / expression-language conversion experience AI / LLM Engineering Prompt engineering / structured (schema-constrained) output generation LLM API integration (Anthropic, OpenAI, Azure OpenAI) RAG or classification pipeline experience Coding Agent experience via Claude / Github copilot Eval-harness / model output scoring experience Integration / Ecosystem MCP (Model Context Protocol) server development Azure CLI / Azure AD authentication flows REST API integration & webhook handling Testing & Quality Test automation for workflow/flow validation Application level testing best practices Nice to Have (General) SharePoint / M365 admin experience   AI Lead Skill: All Developer-level skills (Full Developer Skills Matrix (see 'Developer Skills' tab)) Architecture Designs multi-stage for AI Coding Architectures (Agents, Skills, Harness Engineering) Experience architecting AI-in-the-loop systems (bounding LLM output, hallucination/fidelity risk) Azure cloud architecture (App Services, Functions, Key Vault, Azure AD) AI/ML Strategy Build-vs-buy calls on model selection, fine-tuning vs. prompting, cost/latency tradeoffs Defines evaluation metrics for a generative system (fidelity scoring, gap analysis frameworks) Delivery & Stakeholder Management Has scoped feasibility studies vs. production builds Client-facing experience; triages edge-case escalations into a backlog Defines data requirements with customer IT teams (logs, connector inventories, run history) Risk & Compliance Migration risk management (unsupported actions, rollback strategy) Security review experience (auth handling, credential migration, connection ownership) Leadership Has led a small-to-mid AI/automation engineering team (3–8 devs) Mentors across both classical engineering and AI/prompt-engineering skill sets