Lead AI Engineer(AI Solution Lead)/Japan - Tokyo, Japan
MetLife · Yes · 2mo ago
Role Type | Technical leadership / hands-on delivery leadership |
Primary Mission | Convert business ideas into delivered AI-enabled products end-to-end. |
Typical Scope | Lead discovery, solutioning, MVP definition, build-vs-buy support, engineering delivery, and production handover. |
Reporting / Team Context | AI Platforms / enterprise application delivery team |
Role Purpose
The Lead AI Engineer acts as the bridge between business stakeholders and engineering delivery. Many business teams begin with an idea rather than detailed requirements; this role leads structured discovery, translates business pain points into manageable requirements, proposes practical solution options, and guides the team from MVP definition through build and delivery.
Key Responsibilities
- Lead business requirement definition workshops; ask the right questions to uncover user pain points, operational constraints, success metrics, and decision criteria.
- Translate high-level ideas into clear user stories, acceptance criteria, solution scope, MVP definition, delivery roadmap, and backlog priorities.
- Create audience-appropriate visual materials such as solution diagrams, process flows, architecture views, MVP comparisons, and decision papers.
- Facilitate multi-round discussions with business, IT, risk/compliance, security, architecture, and vendor teams to align on feasible solutions.
- Support build-vs-buy analysis, including technical feasibility, integration complexity, maintainability, delivery risk, operating model, and cost considerations.
- 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.
- Ensure agile delivery discipline: sprint planning, backlog refinement, dependency tracking, stakeholder demos, and transparent status communication.
Required Technical Skills
- 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.
Required Leadership & Soft Skills
- 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.
Area | Expected Capability |
Discovery | Lead workshops, clarify business pain points, define measurable outcomes, and convert ideas into requirements. |
Solutioning | Create options, diagrams, MVP scope, architecture approach, and recommendation for build/buy decisions. |
Delivery | Lead agile execution, code/design review, CI/CD readiness, release planning, and production handover. |
Stakeholder Management | Communicate clearly with business users, IT, architecture, compliance, security, vendors, and senior leaders. |
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.
Success Measures
- Business ideas are converted into clear, prioritized, and deliverable requirements.
- Stakeholders can understand solution options and make informed MVP/build-vs-buy decisions.
- AI solutions are delivered with production-quality engineering practices and clear operational ownership.
- Junior engineers grow through coaching, review, and structured delivery guidance.