19119 AIエンジニア/Japan(GG9) (#571)

The AI Engineer works with business stakeholders, solution leads, architects, and engineering peers to transform business needs into working AI-enabled products. The role requires strong hands-on development capability, an agile delivery mindset, and the ability to communicate clearly with both technical and business audiences.

Key Responsibilities

• Support requirement clarification by asking practical questions, identifying assumptions, and helping break business needs into manageable technical tasks.
• Develop proof of concepts, prototypes, MVP components, and production-ready features for AI and agentic applications.
• Build and maintain Python services, APIs, data pipelines, LLM orchestration flows, prompt/context logic, and integration components.
• Use GitHub Enterprise or equivalent tooling for source control, pull requests, code review, branching, and release collaboration.
• Implement automated testing, CI/CD pipelines, containerized deployments, monitoring hooks, and environment configuration.
• Collaborate with cloud, security, architecture, data, and operations teams to meet enterprise delivery standards.
• Participate actively in agile ceremonies including daily standups, sprint planning, backlog refinement, demos, and retrospectives.
• Document technical designs, setup steps, known limitations, operational runbooks, and support notes clearly.

Required Technical Skills

• Cloud-based development experience on Azure, AWS, Google Cloud, or similar platforms; Azure experience preferred.
• Strong Python programming skills for backend development, data processing, automation, and AI application development.
• Data engineering fundamentals, including data pipelines, APIs, structured/unstructured data handling, validation, and transformation.
• Experience with LangChain, LangGraph, Semantic Kernel, AutoGen, or similar frameworks for LLM/agentic application development.
• Understanding of LLM concepts including prompt engineering, context engineering, retrieval patterns, evaluation, and error analysis.
• GitHub Enterprise, GitHub Actions, Azure DevOps, or equivalent source control and CI/CD tooling experience.
• Docker and Kubernetes fundamentals for packaging, deployment, configuration, and runtime troubleshooting.
• Testing and quality practices including unit tests, integration tests, regression checks, and secure coding basics.
• 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) fundamentals and practical ability to implement or integrate MCP-based tools/resources for agentic applications.

Required Soft Skills

• Proactive communication: raises risks, blockers, assumptions, and progress clearly without waiting to be asked.
• Curiosity and problem-solving mindset: investigates business context and technical root causes, not only assigned tasks.
• Collaboration skills: works effectively with business users, senior engineers, architects, remote members, and vendors.
• Learning agility: can quickly pick up new frameworks, cloud services, LLM patterns, and enterprise delivery standards.
• Quality ownership: takes responsibility for maintainable code, clear documentation, testing, and operational readiness.
• Ability to explain technical work in simple business language when needed.

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 teams.
• Japanese business communication ability is a plus for Japan-based stakeholder discussions.
• Experience with RAG pipelines, vector search, knowledge article ingestion, conversation analytics, or AI evaluation frameworks.

Success Measures

• Features are delivered with good quality, maintainability, and clear documentation.
• Business requirements are implemented accurately and validated through demos or acceptance criteria.
• CI/CD, testing, and deployment practices reduce manual work and delivery risk.
• The engineer contributes proactively to team learning, issue resolution, and continuous improvement.