About this role
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 enginesRegex / expression-language conversion experienceAI / LLM Engineering
Prompt engineering / structured (schema-constrained) output generationLLM API integration (Anthropic, OpenAI, Azure OpenAI)RAG or classification pipeline experienceCoding Agent experience via Claude / Github copilotEval-harness / model output scoring experienceIntegration / Ecosystem
MCP (Model Context Protocol) server developmentAzure CLI / Azure AD authentication flowsREST API integration & webhook handlingTesting & Quality
Test automation for workflow/flow validationApplication level testing best practicesNice 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 tradeoffsDefines evaluation metrics for a generative system (fidelity scoring, gap analysis frameworks)Delivery & Stakeholder Management
Has scoped feasibility studies vs. production buildsClient-facing experience; triages edge-case escalations into a backlogDefines 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 B.E/B.Tech/M.Sc. Computers/MCA
Beware of scams
Our recruiting team may communicate with candidates via our @hitachisolutions.com domain email address and/or via our SmartRecruiters (Applicant Tracking System) domain email address regarding your application and interview requests.
All offers will originate from our @hitachisolutions.com domain email address. If you receive an offer or information from someone purporting to be an employee of Hitachi Solutions from any other domain, it may not be legitimate.