AI Software Engineer:   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 Skills:  Core Programming & Parsing - Node.js / TypeScript or Python (framework matching the Agent's stack) Core Programming & Parsing - XML/JSON parsing experience (Nintex NWF is XML-based) Core Programming & Parsing - Experience building parsers, ASTs, or rule engines Core Programming & Parsing - Regex / expression-language conversion experience AI / LLM Engineering - Prompt engineering / structured (schema-constrained) output generation AI / LLM Engineering -  LLM API integration (Anthropic, OpenAI, Azure OpenAI) AI / LLM Engineering -  RAG or classification pipeline experience AI / LLM Engineering -  Coding Agent experience via Claude / Github copilot AI / LLM Engineering - Eval-harness / model output scoring experience Integration / Ecosystem -  MCP (Model Context Protocol) server development Integration / Ecosystem -  Azure CLI / Azure AD authentication flows Integration / Ecosystem -  REST API integration & webhook handling Testing & Quality -  Test automation for workflow/flow validation Testing & Quality -  Application level testing best practices Nice to Have (General) - SharePoint / M365 admin experience