Lead AI Engineer(ai Solution Lead)/japan

Lead AI Engineer role focused on converting business ideas into delivered AI-enabled products end-to-end, involving discovery, solutioning, MVP definition, build-vs-buy analysis, and engineering delivery for Azure/cloud-based AI and agentic applications. Requires technical leadership in Python, data pipelines, LLM orchestration, CI/CD, and containerization, with a strong emphasis on consultative communication and stakeholder management.

What you'd actually do

  1. Lead business requirement definition workshops; ask the right questions to uncover user pain points, operational constraints, success metrics, and decision criteria.
  2. Translate high-level ideas into clear user stories, acceptance criteria, solution scope, MVP definition, delivery roadmap, and backlog priorities.
  3. Create audience-appropriate visual materials such as solution diagrams, process flows, architecture views, MVP comparisons, and decision papers.
  4. Facilitate multi-round discussions with business, IT, risk/compliance, security, architecture, and vendor teams to align on feasible solutions.
  5. Support build-vs-buy analysis, including technical feasibility, integration complexity, maintainability, delivery risk, operating model, and cost considerations.

Skills

Required

  • Cloud-based solutioning and development experience; Azure experience strongly preferred, with AWS or Google Cloud also valuable.
  • Python application development for backend services, automation, AI/ML workflows, or data processing.
  • Data engineering experience, including data pipelines, ETL/ELT patterns, API integration, data quality checks, and secure data handling.
  • Experience with LangChain, LangGraph, Semantic Kernel, AutoGen, or similar agentic/LLM application frameworks.
  • Practical understanding of LLM usage, including prompt engineering, context engineering, evaluation, guardrails, retrieval-augmented generation, and model behavior analysis.
  • CI/CD experience using GitHub Enterprise, GitHub Actions, Azure DevOps, or equivalent tooling.
  • Containerization and orchestration experience using Docker and Kubernetes.
  • API design, microservices, authentication/authorization, observability, logging, and operational monitoring fundamentals.
  • Understanding of enterprise security, privacy, compliance, and production change-management expectations.
  • MVP definition and delivery planning: ability to identify the minimum viable product, define scope boundaries, prioritize features, validate assumptions, and create a practical roadmap from prototype to production delivery.
  • Model Context Protocol (MCP) understanding and hands-on ability to design secure tool/resource integration patterns for agentic applications.
  • Strong consultative communication: able to guide business users who do not yet have detailed requirements.
  • Business empathy and problem-framing: able to understand pain points in business terms before jumping to technology.
  • Facilitation and negotiation skills across business, technology, risk, compliance, architecture, and vendor stakeholders.
  • Ability to simplify complex AI/cloud topics for non-technical audiences and provide enough depth for engineering teams.
  • Proactive ownership mindset; comfortable driving ambiguous topics to concrete decisions and deliverables.
  • Coaching mindset: able to mentor junior engineers and improve team delivery capability.
  • Strong written communication for decision papers, diagrams, requirements, status updates, and executive summaries.

Nice to have

  • Experience in insurance, financial services, customer service, call center, underwriting, claims, producer support, or policy administration projects.
  • Experience working with remote and overseas members across different time zones, cultures, and delivery models.
  • Japanese business communication ability is a strong plus for Japan-based stakeholder engagement.

What the JD emphasized

  • Lead hands-on solution design and delivery for Azure/cloud-based AI and agentic applications.
  • Practical understanding of LLM usage, including prompt engineering, context engineering, evaluation, guardrails, retrieval-augmented generation, and model behavior analysis.
  • MVP definition and delivery planning: ability to identify the minimum viable product, define scope boundaries, prioritize features, validate assumptions, and create a practical roadmap from prototype to production delivery.
  • Model Context Protocol (MCP) understanding and hands-on ability to design secure tool/resource integration patterns for agentic applications.

Other signals

  • Lead hands-on solution design and delivery for Azure/cloud-based AI and agentic applications.
  • Provide technical leadership across Python, data pipelines, LLM orchestration, CI/CD, containerization, and cloud-native engineering practices.
  • Guide engineers through design reviews, code reviews, testing strategy, deployment readiness, production support planning, and continuous improvement.