Lead AI Engineer(AI Solution Lead)/Japan

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 Lang Chain, Lang Graph, Semantic Kernel, Auto Gen, 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.