Aiエンジニア/japan(gg10)

AI Engineer role focused on building and deploying AI-enabled applications and agentic systems within an enterprise context. Responsibilities include full software development lifecycle for AI features, LLM orchestration, data pipelines, CI/CD, and cloud deployment.

What you'd actually do

  1. Support requirement clarification by asking practical questions, identifying assumptions, and helping break business needs into manageable technical tasks.
  2. Develop proof of concepts, prototypes, MVP components, and production-ready features for AI and agentic applications.
  3. Build and maintain Python services, APIs, data pipelines, LLM orchestration flows, prompt/context logic, and integration components.
  4. Use GitHub Enterprise or equivalent tooling for source control, pull requests, code review, branching, and release collaboration.
  5. Implement automated testing, CI/CD pipelines, containerized deployments, monitoring hooks, and environment configuration.

Skills

Required

  • 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.

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.

What the JD emphasized

  • LLM orchestration
  • agentic applications
  • MVP definition and delivery planning
  • prompt engineering
  • context engineering

Other signals

  • Build, test, deploy, and improve AI-enabled applications that solve business problems.
  • 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.